1 Introduction
A large number of cameras recently have been deployed to cover wide area. Besides, tracking multiple targets in a camera network becomes an important and challenging problem in visual surveillance systems since inperson monitoring wide area is costly and needs a lot of effort. Hence, it is desirable to develop multitarget multicamera tracking (MTMCT) algorithm.
In this paper, our goal is to develop an algorithm that can track multiple targets (especially for pedestrians in this work) in a camera network. The targets may move within a camera or move to another camera and the coverage of each camera does not overlap. To achieve this goal, we need to solve both single camera tracking (SCT) and multicamera tracking (MCT). There has been great amount of effort made to SCT whereas relatively smaller amount of effort has been done for MCT with disjoint views. Moreover, most MCT approacheschen2014object ; javed2003tracking ; wang2014distributed only focus on tracking targets across cameras by assuming solved SCT in advance; thus, jointly tracking multiple targets in both within and across cameras still remains to be explored much furthertesfaye2017multi .
The proposed MHT algorithm tracks targets across cameras by maintaining the identities of observations which are obtained by solving SCT that tracks targets in withincamera. Thus, our method jointly tracks targets in both within and across cameras. In this work, we adopt the realtime and online methodristani2014tracking to produce observations by tracking multiple targets in withincamera. These observations obtained from each camera are fed into the proposed MHT algorithm which solves MCT problem. The proposed MHT algorithm forms trackhypothesis trees with obtained observations either by adding a child node to hypothesis tree, which describes the association between an observation and an existing track hypothesis, or by creating a new tree with one root node indicating an observation, which describes the initiation of a new multicamera track. Each branch in trackhypothesis trees represents different across camera data association result (i.e., a multicamera track). To work in concert with SCT, every node in trackhypothesis trees designates certain observation and all leaf nodes have a status. There are three statuses for the proposed MHT and each of which represents a different stage of a multicamera track, tracking, searching, and endoftrack. With the status, the MHT can form the trackhypothesis trees while simultaneously solving SCT to produce observations. Then it selects the best set of track hypotheses as the multicamera tracks from the trackhypothesis trees. Furthermore, we propose gating mechanism to eliminate unlikely observationtotrack pairing; this also prevents trackhypothesis trees from unnecessary growth. We propose two gating mechanisms, speed gating and temporal gating in order to deal different tracking scenarios (tracking targets on the ground plane or image plane).
For the appearance feature of an observation, we used simple averaged color histogram as an appearance model after Convolutional Pose Machinewei2016cpm is applied to an image patch of a person in order to capture the pose variation. The experimental results shows that our method achieves stateoftheart performance on DukeMTMC dataset and performs comparable to the stateoftheart method on NLPR_MCT dataset. Furthermore, we demonstrate that proposed method is able to operate in realtime with realtime SCT in Section 4.5.
The remainder of this paper organized as follows. In Section 2, we review relevant previous works. The detailed explanation of proposed method is given in Section 3. Section 3.1 describes how the proposed MHT forms trackhypothesis trees while it simultaneously works with SCT. The proposed gating mechanism is explained in Section 3.2. In Section 4, we report experiment results with conducted on DukeMTMC and NLPR_MCT datasets. Finally, we conclude the paper in Section 5.
2 Related Works
Single camera tracking (SCT), which tracks multiple targets in a single scene, is also called multiobject tracking (MOT). Many approaches have been proposed to improve the MOT. Trackbydetection, which optimizes a global objective function over many frames have emerged as a powerful MOT algorithm in recent yearswang2016tracking . Network flowbased methods are successful approaches in trackbydetection techniquesberclaz2011multiple ; zhang2008global ; pirsiavash2011globally . These methods efficiently optimize their objective function using the pushrelabel methodzhang2008global and successive shortest path algorithmsberclaz2011multiple ; pirsiavash2011globally . However, the pairwise terms in network flow formulation are restrictive in representing higherorder motion models, e.g., linear motion model and constant velocity modelcollins2012multitarget . In contrast, formalizing multiobject tracking with multidimensional assignment (MDA) problem produces more general representations of computed trajectories since MDA can exploit the higherorder informationcollins2012multitarget ; mhtrv . Solutions for MDA are MHTmhtold ; mhtrv ; papageorgiou2009maximum
and Markov Chain Monte Carlo (MCMC) data association
oh2009markov . While MCMC data association exploits the stochastic method, MHT searches the solution space deterministic way.Multiple Hypothesis Tracking(MHT) was first presented in reid1979algorithm
and is regarded as one of the earliest successful algorithm for visual tracking. MHT maintains all track hypotheses by building trackhypothesis trees whose branch represent a possible data association result(a track hypothesis). The probability of a track hypothesis is computed by evaluating the quality of data association result the branch had. An ambiguity of data association which occurs due to either short occlusion or missed detection does not usually matter for MHT since the best hypothesis is computed with higherorder data association information and entire track hypotheses. In this paper, we applied MHT to solve the multicamera tracking problem.
Multicamera tracking aims to establish target correspondences among observations obtained from multiple cameras so as to achieve consistent target labelling across all cameras in the camera network tesfaye2017multi . Earlier research works in MCT only try to address tracking targets across cameras, assuming solved SCT. However, researchers have argued recently that assumptions of availability of intracamera tracks are unrealistic wang2014distributed . Therefore, solving MCT problem by simultaneously treating problem of SCT seems to address more realistic problem. Y.T Tesfaye tesfaye2017multi proposed a constrained dominant set clustering (CDSC) based framework that utilizes a three layers hierarchical approach, where SCT problem is solved using first two layers, and later in the third layer MCT problem is solved by merging tracks of the same person across different cameras. In this paper, we also solve the problem of across camera data association(MCT) by the proposed MHT while SCT is simultaneously treated by realtime multiobject tracker such as ristani2014tracking ; song2016online .
Multicamera tracking with disjoint views is a challenging problem, since illumination and pose of a camera changes across cameras as well as a track discontinues owing to the blind area of camera network or miss detections. Some MCT methods try to relax the variation in illumination using appearance cue. O. Javed javed2008modeling suggested the brightness transfer function to deal with illumination change as a target moves across cameras. B.J. Prosser prosser2008multi used Cumulative Brightness Transfer Function that can learn from very sparse training set and A. Gilbert gilbert2006tracking proposed incremental learning method to model the color variations. S. Srivastaba srivastava2011color suggested color correction method for MCT in order to achieve color consistency for each target across cameras. Recent methods have used not only the appearance cue but also the spacetime cue to improve the performance of MCT. C. Kuo kuo2010inter first learned an appearance model for each target and combined it with spacetime information. They show that their proposed combined model improved the performance of acrosscamera tracking. S. Zhang zhang2015tracking tracked multiple interacting targets by formulating the problem into network flow problem. They identified the group merge and split events using spacetime relationship among targets. In chen2014object
, they learn acrosscamera transfer model using both spacetime and appearance cues. They designed spacetime transfer model as normal distribution and learned the parameters using crosscorrelation function. For appearance transfer model, they used color transfer method to capture color variations across the cameras. Ergys solved SCT
ristani2014tracking by transforming the problem into graph partitioning problem and extending their approach to multicamera tracking with disjoint views. There are some approaches to solve MCT problem using person reidentification revpp1 ; revpp2 . L. Chen revpp1proposed a deep neural network architecture composed of convolutional neural network (CNN) and recurrent neural network (RNN) that can jointly exploit the spatial and temporal information for the videobased person reidentification. C.W. Wu
revpp2designed a trackbased multicamera tracking (TMCT) framework with person reidentification algorithms. Their method found multicamera tracks using reidentification algorithms as both the feature extractor of an object and the distance metric between two object. They also proposed new evaluation metrics for MCT to report the performance of TMCT with various reidentification algorithms.
3 Method
For a multicamera multitarget tracking system with disjoint views, the set is the set of all cameras in the camera network, where is the number of cameras. Let denote the most recent time. Single camera tracker of each camera generates observations so that they form a set of observations where is the total number of observations observed until the most recent time . An observation contains all the information about the target while it was being tracked by a single camera tracker. Specifically, the th observation consists of the appearance feature , the track and the camera in which it appears . The track is a collection of all track histories of observation observed in , i.e. where represents the length of track of recorded until time and refers the th track history of containing time stamp , position and size in image plane of camera , and is a position on ground plane. Note that depending on the scenario, might not contain . With this observation set, the MCT system outputs a set of multicamera tracks, , where is the size of set and refers to the th multicamera track which consists of observations, i.e. a number of observations(or single observation) which have the same identity composes a multicamera track. The is an index set whose elements indicate elements of set ; hence, is a subset of . Here, we introduce a notation enumerating the set and (the th element of ) indicates an observation that is also the th observation of . Thus, we can write , which means that the th observation of the multicamera track is the in the set . Therefore, is not only the number of elements in but also the number of observations that the had. Finally, for all observations and all multicamera tracks there is the constraint that one observation must belong to a unique multicamera track such that:
(1) 
i.e. all tracks in the set do not conflict each other.
3.1 Multiple Hypothesis Tracking for MCT
In this section, we introduce how a trackhypothesis tree is formed for a multicamera tracking system. The tree of our method maintains multicamera tracks by initiating, terminating, updating with new observations. A node of the tree represents an observation that is generated by SCT. All branches of the tree represent all possible hypotheses that originate from a single observation or root node. A key strategy of MHT is to delay data association decisions by keeping multiple hypotheses active until data association ambiguities are resolvedmhtrv . As new observations are received, MHTblackmanbook forms new trees to initiate tracks for each new observation. Then existing tracks are updated with new observations that were within the gate. Moreover, all existing tracks are updated with dummy observations in order to describe the hypothesis that they are not updated with any current observation(missing detection). Consequently, the number of track hypotheses continues to expand and many of the tracks are inconsistent since the same observations are used for more than one track.
In the tracking literature, a scan is the time interval and sensor FoV (field of view) where observations are collectedblackmanbook . With previous MHT algorithms for vision based target tracking systemsmhtrv ; mhtold , images were scanned framebyframe to gather observations using a feature detector such as person detectorfelzenszwalb2010object and corner detectorlucas1981iterative . Hence, the depth of their trackhypothesis trees grow with every frame. However, unlike their approach, we gather observations by scanning the entire camera network within a fixed amount of time; consequently, our tree is extended after a scan. Setting an appropriate time interval for one scan is important, because a scan should not contain multicamera tracks. For example, if the interval is long enough to have observations that could form a multicamera tracks; then the system loses the chance to associate them correctly(Figure (a)a). On the other hand, tooshort time interval for a scan leads to increased computational overhead as well as deepened trackhypothesis tree due to the frequent update of trees. The amount of time for a scan generally depends on the datasets. Furthermore, to prevent the trees from growing meaninglessly by appending only dummy node to all branches, trees are extended with dummy only when a scan contains new observations (Figure (b)b). This enhances the efficiency of tree formation if pedestrians enter into the camera network sparsely.
Now we introduce the three statuses . Every leaf node of our trackhypothesis trees should have a status in order that a branch, or a track hypothesis, has a status. Note that intermediate nodes have no effect on the status. The first status, , is tracking status, meaning that the target is being tracked by a single camera tracker. Therefore, all tracks are initiated with status and the track hypothesis in this status is not updated with new observations. If the target disappears from a camera, the status of leaves which refer to the target changes to searching, . A leaf node with status is updated with new observations that satisfy the gating condition. The last status, , is endoftrack, which means that the target exits the camera network due to its invisibility for a long period of time. A branch with this status will not be updated with new observations except with dummy observations. A leaf with status changes to status by checking the elapsed time from when the leaf began status to most recent time (refer to Section 3.2). Note that only leaf nodes in status can be updated with new observations by appending a node referring to a new observation as its children. Otherwise, a leaf node in either status or only appends a dummy observation indicating the same observation as its parent after the scan that receives any new observations. Thus, once the status of a track hypothesis has changed to , it can not revert to either or . From here, we explain how we form the tree using the example in Figure (a)a. In Figure (b)b, after the first scan, a tree(tree 1) with a node indicating observation is formed to make a track hypothesis that initiates a new multicamera track. After the second scan, the status of the leaf node of tree 1 changes to because is no longer seen by Camera 1. In the third scan, two new observations, and , are received; hence, two trees, tree 2 and tree 3, are newly formed for and , respectively. Then, the existing track hypothesis in status (the root node of tree 1) is associated with new observations by appending nodes referring to and , respectively. A dummy node is also added in order to describe the hypothesis that both and were not (Figure (c)c). Note that we only consider the temporal gating scheme in this example for simplicity and the gating time of covers initiation time of both observations, and . After the third scan, because the gating time of is expired, the status of leaf node referring to is changed to (rightmost leaf of tree 1 in Figure (c)c). Next, two targets exit from the Camera 2 in the fourth scan. The status of the leaf nodes related to the exited targets is changed. For , the status of two leaves are changed to because occurred twice in the trackhypothesis trees(leftmost leaf node of tree 1 and root node of tree 2 in Figure (d)d). This works again for . Because no new observation is received in this scan, trees are not extended with dummy nodes. Finally, Figure (e)e shows the result of trackhypothesis tree formation after the sixth scan. With these track trees, all possible data association hypotheses can be made. The first branch of tree 1 shows a multicamera track with a hypothesis that describes that the observations and have the same identity, while the last branch of tree 1 shows that the multicamera track consists of only one observation, (Figure (e)e).
To compute the set that satisfy the constraint (1), each track hypothesis should manage the incompatible track lists. For example, in Figure (e)e, the incompatible tracks of the first branch of tree 1 are the track hypotheses that have observation , and , i.e. all the other branches in same the tree (because they at least share ), all track hypotheses in tree 2(because of and ), the first branch of tree 3(because of ), and the root node of tree 4(because of ). Then the set of best track hypotheses is computed by solving a maximum weighted independent set problemmhtrv which will be described in the Section 3.5.
3.2 Gating
Gating is a technique for eliminating unlikely observationtotrack pairings. In mhtrv ; mhtold , the spatial distance between the predicted location of an existing track and a newly received observation was used to determine whether to update an existing track with a new observation. They used the velocity of existing tracks to compute the predicted locations. If the distance between the predicted location and observation exceeds a predetermined threshold, the track is not updated with that observation. However, for multicamera tracking with disjoint views, predicting the location of reappearance is very difficult because targets move through blinded areas for a long time. To resolve this problem, distance between observations is used instead of predicting the location. Note that, in this case, the world coordinates of any given track are known. Let be the last observation of an existing track and be the newly received observation. Then the following inequality checks the speed gating,
(2) 
where is the world coordinate of the th track of on ground plane, is the minimum speed of a target, and is the maximum speed of a target. These can either be set by the system designer or learned from training samples. This assumed that a target can not move faster (slower) than the threshold. is the control parameter of the distance metric between Euclidean and Manhattan distance, , which is also set by the system designer. This parameter is beneficial since we do not know what will transpire in blind areas. If an observationtotrack pair does not satisfy the inequality, then that pair will not be associated. A leaf node in status that refers to observation changes status to if specified time, , has elapsed after when the leaf node began the status . That is, if , then the leaf node change status to , where is the most recent time. is computed by where is the area of the ground plane of the camera network and
is the estimated speed of
.In some situations, it is impossible to locate targets that are in tracking status() due to the absence of calibration information and map information. In this case, we use temporal gating instead of speed gating. First, we estimate entry/exit points for each camera either by learning from training samples or by getting information from the system designer. Let be the set of entry/exit points, and is the th entry/exit point, where represents the total number of entry/exit points in the camera network. Then of has two elements, and , which represent the entry and exit point of the observation, respectively, i.e. , and
. After that, we learned the transition matrix between each pairs of entry/exit points as well as the mean and standard deviation of transition time using training samples. Let
be the transition matrix, a square matrix with size . An element , th row and th column, is set to one if a transition from to exists. Otherwise, it is set to zero. For all , we learned the mean and standard deviation matrix of transition time with training samples. Then the temporal gating for the existing track whose leaf node designates and newly received observation checks the followings:(3) 
where and are the minimum and maximum threshold for temporal gating, and which can be learned from the training samples. Note that middleterm of the inequality is always positive for MCT with disjoint view, otherwise its absolute value is needed. If the mean transition time between and is and its standard deviation is , and could be set to and , respectively, where , are set by the system designer. If an observationtotrack pair does not satisfy the check, then that pair will not be associated. A leaf node in status referring to observation changes the status to if the time gap between last observed time and recent time is beyond the predetermined gating time, i.e. it will change the state to if is larger than . In this case, is used for all observations.
3.3 Pruning
Since there is potential for a combinatory explosion in the number of track hypotheses that our MHT system could generate, pruning the trackhypothesis tree is an essential task for MHT. We adopted the standard scan pruning techniqueblackmanbook ; mhtrv . The standard scan pruning algorithm assumes that any ambiguity at is resolved by time , i.e. it defines the number of frames to look ahead in order to resolve an ambiguitymhtold . Note that in our case, refers not to the number of frames but to the number of scans since our trees grow after the scan where any new observation is received. An example of scan pruning is described in Figure (f)f, where . First, finding the best track hypothesis set is needed before pruning the trees. Computing the best track hypothesis set using the track score is described in Section 3.4 and 3.5. After we identify the best hypothesis set, we ascend to the parent node times from each selected leaf node to find the decision node. At that node, we prune the subtree that diverged from the best track. Consequently, we have a tree of depth below the decision node, while the tree is degenerated into simple list of assignments above the decision node.
3.4 Scoring a track
The evaluation of a track hypothesis should deal all aspects of data association quality that a multicamera track possess. According to the original formulationblackmanbook , we define a likelihood ratio() of a track to be
(4) 
where hypotheses and are the true target and false alarm hypotheses of given combination of data, i.e.
is probability density function evaluated with given data
under the assumption that is correct. Theis a prior probability of
. The conditional probabilities in Equation (4) can be partitioned into a product of two terms, and , assuming that the appearance and kinematic information of a target are independent each other. Therefore,(5) 
where , the second term and third term in rightmost side are and , respectively. The Equation (5
) can be further factorized by chainrules:
(6) 
where assuming that received observations are conditionally independent under the false alarm hypothesis. and are the appearance and kinematic likelihood ratio when the th observation is associated with the existing track .
For likelihood of the kinematic term, , we define two different measures in order to differentiate the tracking scenarios. The first one is for the scenario that tracking targets on the image plane of each camera is only available. In this case, we assumed that transition time across cameras is normally distributed, i.e.
(7) 
where the mean
and variance
would be estimated using training samples which moved from to . Note that we dropped subscript for and for simplicity. They should be learned for all pairs of possible transitions (i.e., for all where ). The is the time stamp of the initiation time of whereas is the time stamp of the last observed time of .The other kinematic likelihood function is for the scenario where we can locate a target on the ground plane of the camera network; hence, measuring distance between tracks is feasible. Let be the moving speed of an observation and it is estimated by averaging:
(8) 
Then the likelihood function is also assumed to be Gaussian:
(9) 
where is the estimated travel distance of , and (which comes from the Equation (2)) is distance between and . Note that although both and are function of and we dropped them for the simplicity of notation. The
is the precision for the Gaussian distribution. For the false alarm hypothesis of kinematic term,
, we set to constant probability, .To compute the appearance likelihood, we first built an color histogram for the appearance feature of observation while it has tracked. The learned appearance model, , is constructed for the track , after each association between the existing track and a new observation is made, i.e.
(10) 
where is learned appearance feature for the track . Thus, is the averaged feature over
associated observations. This averaging model is used because if the track hypothesis consistently associated the observations which had the same identity then the averaged feature would have the ability to classify correctly than that of inconsistently associated track due to its stable distribution of colors. Then the appearance likelihood is computed by comparing two histogram:
(11) 
where is similarity measure between two histograms and it can be any metric such as, Bhattacharyya, histogram intersection, earth mover distance and so on. However, some metrics should be modified in order to use it as the probability (i.e., ). The false alarm hypothesis of appearance term, , is the constant probability .
Next, we define the log likelihood ratio, or score, for a multicamera track, consists of observations, which is the sum of kinematics and appearance related terms plus the initiation score. That is:
(12) 
where is track initiation score and we set it to a constant . Then the score of a track can be computed recursivelyblackmanbook :
(13) 
where is the score of track and is the increment that occurs upon update with a new observation, . Finally, we introduce the weights, and , that control contribution of appearance and kinematics to the score, respectively:
(14) 
where . The score is continuously updated as long as the track hypothesis is updated with a new observation.
3.5 Computing the Best Hypothesis Set
In this section, we describe how the best hypothesis set is computed among all trackhypothesis trees that maintain all the possible multicamera tracks using all observations. The best hypothesis set is computed every scan if the scan received any new observation, and then tree pruning is performed to avoid exponential growth of trees.
To compute the best hypothesis set, we adopt the approach of mhtrv . They change the task to the Maximum Weighted Independent Set (MWIS) problem. MWISpapageorgiou2009maximum is equivalent to the MultiDimensional Assignment (MDA) problem in the context of MHTmhtrv . MDA is used to compute the most probable set of tracksblackmanbook ; poore1993data and its MDA formulation for MHT was introduced in blackmanbook ; poore1993data .
Let be the undirected graph for MWIS, which corresponds to a set of trackhypothesis trees generated by MHT. Then the solution of MWIS can be determined by solving the discrete optimization problem:
(15) 
Each vertex is assigned to a track hypothesis as well as a vertex has weight that corresponds to its track score, . There is an undirected edge, , linking two vertices and if the two tracks are incompatible due to shared observations, i.e. if they are violating constraint (1). Therefore, the constraint in Equation (15) is a discretized form of constraint (1). In the graph , an independence set is a set of vertices no two of which are adjacent, i.e. all tracks in the independence set are compatible. The maximum weighted independence set in is an independence set with maximum total weight, i.e. a set of compatible tracks of which the total track score is maxima. An example of the MWIS graph is shown in Figure 3. It corresponds to the set of trackhypothesis trees in Figure (e)e. Each vertex represents a track hypothesis(each branch of a tree) and the observations used for that track are shown at the vertex with threedigits, where zero denotes a dummy observation. Note that since three scans(first, third and fifth scans) have received observations (among the six scans in Figure (a)a) a track can contain at most three observations(Refer to Section 3.1 for more details). The red vertices are selected for the best hypothesis set in the example(Figure 3). We used the Gurobi optimizer to solve the above MWIS problem.
4 Experiments
We evaluated our method using two datasets: DukeMTMC ristani2016MTMC and NLPR_MCT mct2014 . These datasets were designed for a multicamera tracking system. The DukeMTMC dataset consists of eight synchronized cameras, which was recorded at 1080p resolution and 60 fps. The dataset contains more than 7,000 single camera trajectories and over 2,000 unique identities over minutes for each camera, a total of more than 10 h. We used IDMeasure ristani2016MTMC with the DukeMTMC dataset to evaluate our multicamera tracking performance. The NLPR_MCT dataset provides four different videos that are, at most, minutes long, and they all have a resolution of . To measure the performance for this dataset, we used the MCTA metricmct2014 . Table 1 shows the parameter setting for each dataset in this section. Note that is in meters per second and is in seconds.
dataset  pruning  scan time  
DukeMTMC  10  0.8  0.001  0.3  0.75  1 sec  0.7, 0.5, 2.0  N/A 
NLPR_MCT  10  0.815  0.005  0.1  0.75  1 sec  N/A  , 
To solve the SCT problem as well as to generate observations that is inputted to the proposed MHT, we used online and realtime MOTristani2014tracking for each camera. Utilizing the online and realtime single camera tracker would not affect the online and realtime capability of unified framework. In Section 4.5, we show that our unified framework works in realtime provided that the SCT problem is solved in realtime.
4.1 Appearance Modeling



In this subsection, the appearance model for an observation is described. The appearance feature is the averaged histogram of observation that is learned while under tracking status() of . To be more specific, let be the extracted feature from corresponding image location , which is the th track histroy of observation . Then the appearance feature that is computed from start to the th track history is:
(16) 
where . Note that to keep the online nature of the proposed method, is used to compute Equation (11) instead of .
For appearance feature, we use an HSV (hue, saturation, value) color histogram for upper and lower body parts where the bin size is 16, 4 and 4 for Hue, Saturation and Value channels respectively. Furthermore, to capture the pose variations of a person, we use the Convolutional Pose Machinewei2016cpm to estimate the pose of a given image patch of a person. The estimated pose of a person is depicted in Figure (a)a. To extract the upper body part, four joints(right shoulder, right hip, left shoulder, left hip) are used(Figure (b)b). Six joints(right hip, right knee, right ankle, left hip, left knee, left ankle) are used for the bottom part of the body (Figure (c)c). Once those body parts are extracted from an image patch, an HSV histogram, , is computed.
Even though we have used color histogram as the appearance feature and simple averaging model in this work, other online appearnce model can be applied to proposed MHT method since it is known that MHT can be extended by including online learned discriminative models without difficulty mhtrv .
4.2 DukeMTMC dataset
Cam  method  IDMeasure  CLEAR MOT  
IDF1  IDP  IDR  MOTA  MOTP  FAF  MT  ML  FP  FN  IDs  
1  ristani2016MTMC  57.3  91.2  41.8  43.0  79.0  0.03  24  46  2,713  107,178  39  
tesfaye2017multi  76.9  89.1  67.7  69.9  76.3  0.06  137  22  5,809  52,152  156  
Ours  84.3  89.7  79.6  84.9  79.5  0.04  191  12  3,679  25,318  55  
2  ristani2016MTMC  68.0  69.3  67.1  44.8  78.2  0.51  133  8  47,919  5,374  60  
tesfaye2017multi  81.2  90.9  73.4  71.5  74.6  0.09  134  21  8,487  43,912  75  
Ours  81.9  88.9  75.9  78.4  77.1  0.07  151  8  6,390  33,377  81  
3  ristani2016MTMC  60.3  78.9  48.8  57.8  77.5  0.02  52  22  1,438  28,692  16  
tesfaye2017multi  64.6  76.3  56.0  67.4  75.6  0.02  44  9  2,148  21,125  38  
Ours  69.3  76.2  63.5  65.7  77.0  0.06  58  7  5,908  18,589  22  
4  ristani2016MTMC  73.5  88.7  62.8  63.2  80.2  0.02  36  18  2,209  19,323  7  
tesfaye2017multi  84.7  91.2  79.0  76.8  76.6  0.03  45  4  2,860  10,686  18  
Ours  80.7  84.1  77.6  79.8  80.1  0.04  47  3  3,633  8,173  17  
5  ristani2016MTMC  73.2  83.0  65.4  72.8  80.4  0.05  107  17  4,464  35,861  54  
tesfaye2017multi  68.3  76.1  61.9  68.9  77.4  0.10  88  11  9,117  36,933  139  
Ours  73.7  81.4  67.3  76.6  80.0  0.05  110  8  4,410  30,195  83  
6  ristani2016MTMC  77.2  84.5  69.1  73.4  80.2  0.06  142  27  5,279  45,170  55  
tesfaye2017multi  82.7  91.6  75.3  77.0  77.2  0.05  136  11  4,868  38,611  142  
Ours  83.5  88.9  78.8  82.8  80.2  0.06  163  6  5,478  27,194  69  
7  ristani2016MTMC  80.5  93.6  70.6  71.4  74.7  0.02  69  13  1,395  18,904  23  
tesfaye2017multi  81.6  94.0  72.5  73.8  74.0  0.01  64  4  1,182  17,411  36  
Ours  81.5  91.4  73.5  77.0  75.5  0.01  69  7  1,232  15,119  33  
8  ristani2016MTMC  72.4  92.2  59.6  60.7  76.7  0.03  102  53  2,730  52,806  46  
tesfaye2017multi  73.0  89.1  61.0  63.4  73.6  0.04  92  28  4,184  47,565  91  
Ours  79.9  90.8  71.3  71.6  75.3  0.05  125  21  4,850  35,288  46  

ristani2016MTMC  70.1  83.6  60.4  59.4  78.7  0.09  665  234  68,147  361,672  91  
tesfaye2017multi  77.0  87.6  68.6  70.9  75.8  0.05  740  110  38,655  268,398  693  
Ours  80.3  87.3  74.4  78.3  78.4  0.05  914  72  35,580  193,253  406  
MultiCam  ristani2016MTMC  56.2  67.0  48.4  
tesfaye2017multi  60.0  68.3  53.5  
Ours  65.4  71.1  60.6 
The DukeMTMC is a large, fullyannotated, calibrated dataset that captures the campus of Duke University, and was recorded using eight fixed cameras. The dataset has an RoI (region of interest) area for each camera where an evaluation is made. The topology of the fixed cameras is shown in Figure 5 where there is no field overlap between any pair of cameras(Figure (b)b). The cameras used to acquire the dataset were synchronized and recorded at 1080p resolution and 60fps. The dataset contains more than 7,000 single camera trajectories and over 2,000 unique identities captures during minutes of recording for each camera, thus, a total of more than 10 h. The video was split into one training/validation set and two test sets, testeasy and testhard set. The difficulty of the testeasy set is similar to the training/validation set, and it is 25 minutes long. The testhard set is 10 minutelong videos and contains a group of dozens of people traversing multiple cameras.
The evaluation criteria for the DukeMTMC dataset is IDMeasure ristani2016MTMC , which measures how well a tracker determine who is where at all times. This criteria has three measures: IDP (Identification precision), IDR (Identification Recall), and
IDF1 (Identification FScore)
. The IDP (IDR) is the fraction of computed (ground truth) detections that are correctly identified. IDF1 is the ratio of correctly identified detections over the average number of groundtruth and computed detections. This process is different from CLEAR MOTBernardin2008 , which reports the amount of incorrect decisions made by a tracker. Moreover unlike CLEAR MOT, it can measure not only the single camera tracking results but also the multicamera tracking results. We reported the single camera tracking performance with both IDMeasure and CLEAR MOT in order to make it clear how much proposed method improved the final tracking performance given these single camera tracks.We compared the quantitative performance of our method with other multitarget multicamera tracking methodsristani2016MTMC ; tesfaye2017multi using the DukeMTMC dataset. The results are shown in Table 2 and Table 3. The evaluation on the Testeasy is shown in Table 2, while the performance on the Testhard is shown in Table 3. In both tables, the last row is for comparison of multicamera tracking performance and the rest are for comparison of single camera tracking performance. We used public detection responses as the input to our method. The results of single camera tracking between ours and ristani2016MTMC were different, even though we used the public single camera tracker that is published by E. Ristani ristani2014tracking , because we modified the original one to fit our multicamera tracking method.^{1}^{1}1The modified version of ristani2014tracking is currently on the web.
https://github.com/yoon28/SCT4DukeMTMC/
The last row of Table 2 shows that the performance of our multicamera tracking method outperformed the stateoftheart methodtesfaye2017multi on the Testeasy sequence by in IDF1, in IDP and in IDR metrics. In the Testhard sequence, the proposed method ranked second with difference of in IDF1 and in IDP metrics, while it was first in IDR metrics (improvement of , Table 3). Finally, the proposed MHT algorithm for the multicamera tracking method outperformed the method of ristani2016MTMC even in the complicated video sequence(Testhard). Even if the ristani2016MTMC ’s average IDF1 over single cameras was higher than ours by , the IDF1 of their multicamera tracking performance was even lower than ours by (Table 3).
Cam  method  IDMeasure  CLEAR MOT  
IDF1  IDP  IDR  MOTA  MOTP  FAF  MT  ML  FP  FN  IDs  
1  ristani2016MTMC  52.7  92.5  36.8  37.8  78.1  0.03  6  34  1,257  78,977  55  
tesfaye2017multi  67.1  83.0  56.4  63.2  75.7  0.08  65  17  2,886  44,253  408  
Ours  64.6  72.2  58.4  61.1  76.7  0.35  78  11  12,570  37,287  394  
2  ristani2016MTMC  60.6  65.7  56.1  47.3  76.5  0.74  68  12  26,526  46,898  194  
tesfaye2017multi  63.4  78.8  53.1  54.8  73.9  0.24  62  16  8,653  54,252  323  
Ours  56.6  61.2  52.6  50.4  74.4  0.68  66  10  24,591  44,401  392  
3  ristani2016MTMC  62.7  96.1  46.5  46.7  77.9  0.01  24  4  288  18,182  6  
tesfaye2017multi  81.5  91.1  73.7  68.8  75.1  0.06  18  2  2,093  8,701  11  
Ours  80.0  86.9  74.1  70.3  76.8  0.07  22  2  2,543  7,737  10  
4  ristani2016MTMC  84.3  86.0  82.7  85.3  81.5  0.04  21  0  1,215  2,073  1  
tesfaye2017multi  82.3  87.1  78.1  75.6  77.7  0.05  17  0  1,571  3,888  61  
Ours  83.3  84.4  82.2  81.2  81.6  0.05  20  1  1,821  2,404  1  
5  ristani2016MTMC  81.9  90.1  75.1  78.3  80.7  0.04  57  2  1,480  11,568  13  
tesfaye2017multi  82.8  91.5  75.7  78.6  76.7  0.03  47  2  1,219  11,644  50  
Ours  85.7  93.3  79.2  81.9  80.1  0.02  52  2  875  10,017  24  
6  ristani2016MTMC  64.1  81.7  52.7  59.4  76.7  0.14  85  23  5,156  77,031  225  
tesfaye2017multi  53.1  71.2  42.3  53.3  76.5  0.17  68  36  5,989  88,164  547  
Ours  54.7  70.0  44.9  56.1  77.8  0.22  82  24  7,902  80,716  423  
7  ristani2016MTMC  59.6  81.2  47.1  50.8  73.3  0.08  43  23  2,971  38,912  148  
tesfaye2017multi  60.6  84.7  47.1  50.8  74.0  0.05  34  20  1,935  39,865  266  
Ours  55.7  74.7  44.4  49.8  73.8  0.11  42  25  4,405  38,687  214  
8  ristani2016MTMC  82.4  94.9  72.8  73.0  75.9  0.02  34  5  706  9,735  10  
tesfaye2017multi  81.3  90.3  73.9  70.0  72.6  0.06  37  6  2,297  9,306  26  
Ours  80.4  93.5  70.5  71.5  74.0  0.02  38  5  731  10,278  10  

ristani2016MTMC  64.5  81.2  53.5  54.6  77.1  0.14  338  103  39,599  283,376  652  
tesfaye2017multi  65.4  81.4  54.7  59.6  75.4  0.09  348  99  26,643  260,073  1637  
Ours  63.5  73.9  55.6  59.6  76.7  0.19  400  80  55,038  231,527  1468  
MultiCam  ristani2016MTMC  47.3  59.6  39.2  
tesfaye2017multi  50.9  63.2  42.6  
Ours  50.1  58.3  43.9 
4.3 NLPR_MCT dataset
The NLPR_MCT dataset consists of four subdatasets. A subdataset is depicted in Figure 6^{2}^{2}2This figure is captured from http://mct.idealtest.org/. Each subdataset includes 35 cameras with nonoverlapping scenes and recordes different situations according to the number of people (ranging from 14 to 255) and the level of illumination changes and occlusionsmct2014 . The videos contain both real scenes and simulated environments. Each video was nearly 20 minutes long (except Dataset 3), with a rate of 25 fps.
In this dataset, the topological connection information for every pair of entry/exit points for each subdataset is provided. We split the of an observation into and , that represent the entry point and exit point of observation , respectively. Because the dataset did not provide separate training and test datasets, we learned the parameters for our method as well as the transition matrix, the mean and standard deviation of transition time for each possible transition pair of entry/exit points using first 70 percent of each dataset.
The evaluation criteria used for the NLPR_MCT dataset was MCTA mct2014 , multicamera object tracking accuracy. It was modified based on CLEAR MOT Bernardin2008 and can be applied to MCT. The metric contains three terms (detection ability, single camera tracking ability and MCT ability), which are multiplied to produce one measure. In this experiment, we used annotated single camera trajectories by assuming that SCT problem is solved in advance^{3}^{3}3 This setting is identical to Experiment of MCT challenge. . We compared the performance of our method with the stateoftheart methods in Table 4. The last column, Avg. Rank, is the averaged ranking over four subdatasets, where the rank was decided by the MCTA score. This criterion is also used in MCT challenge to compare the results with others. The first place for each subdataset was shown in boldface. As a result, both cai2014exploring and our method tied for second place by the Avg. Rank of . However, it is worth noticing that our method has more stable performance than that of cai2014exploring because the rank standard deviation of our method over all subdatasets is while the standard deviation of cai2014exploring is . Again, the MCTA standard deviation of our method over all subdatasets is while that of cai2014exploring is .
Method  NLPR 1  NLPR 2  NLPR 3  NLPR 4  Avg. Rank 
USC_Visioncai2014exploring  0.9152  0.9132  0.5162  0.7051  3.5 
UW_IPLlee2017online  0.9610  0.9265  0.7889  0.7578  1.25 
CRF_UCRchen2016integrating  0.8383  0.8015  0.6645  0.7266  3.75 
EGTrackermct2014  0.8353  0.7034  0.7417  0.3845  4.75 
DukeMTMCristani2016MTMC  0.7967  0.7336  0.6543  0.7616  4.25 
Ours  0.9129  0.8944  0.6699  0.6812  3.5 
used for evaluation.
4.4 Nscan Pruning
In this section, we report how our MHT algorithm is sensitive to the parameter of scan pruning. The scan pruning algorithm assumes that any ambiguity at is resolved by time . The scan pruning was utilized in the multiscan assignment approach to MHT because it solves the data association problems with recent scans of the data thanks to scan pruningblackmanbook ; poore1993data . We evaluated the IDMeasure for various , , on the TrainvalMini of the DukeMTMC dataset. The TrainvalMini is a small part of the training/validation set of the DukeMTMC dataset and is about 18 minutes long sequence. The parameter settings were the same as before except the . The intersectionoverunion is fixed to for this experiment. The experimental result is shown in Figure 7. The result demonstrates that our MHT algorithm is negatively affected by in IDP while it is slightly positively affected in IDR when is increasing. Therefore, the proposed method has a small sensitivity to in terms of IDF1 because the IDP and the IDR are negatively correlated with respect to the . The difference between the minimum(58.02%) and maximum(58.87%) values in IDF1 was 0.85%. The minimum and maximum value of IDP was 64.97% and 66.57%, respectively, while that of IDR was 51.98% and 53.08%, respectively.
4.5 Realtime implementation
In this section, a realtime implementation of the proposed method is described. Full implementation of the proposed method was programmed by Matlab with a desktop PC(Intel i74790K 4.0 Ghz 4core CPU, 16GB RAM, Nvidia GTX 770 GPU and Ubuntu 16.04 OS). For the computation efficiency, we have discarded the Convolutional Pose Machine in the appearance modeling and switched the SCT algorithm from ristani2014tracking to GMPHD(Gaussian Mixture Probability Hypothesis Density) filtersong2016online for realtime implementation. There was no other reasons for switching the SCT algorithm except that we had already implemented the GMPHD in C++. Realtime implementation was developed using Visual C++ with the multithread programming and OpenCV in Windows 10 OS. The test hardware environment included a PC with Intel i77700K 4.5 Ghz 4core CPU, 32GB RAM. To test the processing speed, we have generated a new dataset consisting of six videos with resolution and about 7 minutes long(Figure 8). This dataset, which includes appearance of up to 25 targets, was recorded in the campus of Gwangju Institute of Science and Technology. To detect a person in the dataset, we applied the pedestrian detectorkudet , which processes every frame of each camera to detect pedestrians. The average processing time (includes the processing time of detection, SCT and MCT) was about 15 frames per second for all videos. Note that each video(camera) was processed in parallel by multithread programming. Therefore, this result demonstrates the realtime performance of our method. We used the Gurobi optimizer to solve MWIS problems in this implementations.
5 Conclusion
In this paper, we applied a multiple hypothesis tracking algorithm to handle the multitarget multicamera tracking problem with disjoint views. Our method forms trackhypothesis trees whose branch represents a multicamera track which describes the trajectory of a target that may move within a camera as well as move across cameras. Furthermore, tracking targets within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Besides, two gating schemes have been proposed to differentiate the tracking scenarios. The experimental results shows that our method achieves stateoftheart performance on DukeMTMC dataset and performs comparable to the stateoftheart method on NLPR_MCT dataset. We also show that the proposed method can solve the problem under online and realtime conditions, provided that the single camera tracker solves in such conditions as well.
MHT can be extended by including online learned discriminative appearance models for each track hypothesismhtrv . Therefore, as for the future work, we will investigate online learning techniques that could learn a model for each hypothesis since we used a simple averaging model for appearance modeling in this work.
6 Acknowledgement
This work was supported by Institute for Information and communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. B0101150525, Development of global multitarget tracking and event prediction techniques based on realtime largescale video analysis). In addition, this work also supported by Korea Creative Content Agency (KOCCA) and Ministry of Culture, Sports and Tourism (MCST) (No. R2017050052, Developed intelligent UI/UX technology for AR glassesbased docent operation).
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