Unmanned aerial vehicles (UAVs), a.k.a. drones, get a bad reputation in the media. Most people associate them with negative news, such as flight delays causing by unauthorized drone activities and dangerous attack weapons. However, recent advances in the field of remote sensing and computer vision showcase that the future of UAVs will actually be shaped by a wide range of practical applications[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. To name a few, in the aftermath of earthquakes  and floods 
, UAVs can be exploited to estimate damage[17, 18], deliver assistance, and locate victims. In addition to disaster relief, urban planners are capable of better understanding the environment of a city and implementing data-driven improvements by the use of UAVs [19, 20, 21, 22, 23, 24, 25, 26]. In precision agriculture, agricultural workers can make use of UAVs to collect data [27, 28, 29], automate redundant procedures [30, 31, 32, 33], and generally maximize efficiency [34, 35]. In combination with geospatial information, UAVs are now used to monitor and track animals for the purpose of wildlife conservation [36, 37].
Unlike satellites, UAVs are able to provide real-time, high-resolution videos at a very low cost. They usually have real-time streaming capabilities that enable quick decision-making. Furthermore, UAVs can significantly reduce a dependence on weather conditions, e.g., clouds, and are available on a demand offering a higher flexibility to cope with various problems.
Yet the more UAVs there are in the skies, the more video data they create. The Federal Aviation Administration (FAA) estimates that in the US alone, there are more than 2 million UAVs registered in 2019111https://www.faa.gov/data_research/aviation/aerospace_forecasts/media/Unmanned_Aircraft_Systems.pdf. And every day around 150 terabytes of data can be easily produced by a small drone fleet222https://www.bloomberg.com/news/articles/2017-05-10/airbus-joins-the-commercial-drone-data-wars. The era of big UAV data is here. It is unrealistic for humans to screen these massive volumes of aerial videos and understand their contents. Hence methodological research on the automatic interpretation of such data is of paramount importance.
However, there is a paucity of literature on UAV video analysis, which for the most part is concentrated on detecting and tracking objects of interest, e.g., vehicle and people, in relatively sanitized settings. Towards advancing aerial video parsing, this paper introduces a novel task, event recognition in unconstrained aerial videos, in the remote sensing community. We present an Event Recognition in Aerial video (ERA) dataset, a collection of 2,866 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds (see Fig. 2). This temporal length (5 seconds) corresponds to the minimal duration of human short-term memory (5 to 20 seconds)333http://web.mnstate.edu/smithb/EdPsychWebs/classnotes/coursenotes/chapter_7.htm. This dataset enables training models for richly understanding events in the wild from a broader, aerial view, which is a crucial step towards building an automatic aerial video comprehension system. In addition, to offer a benchmark for this task, we extensively validate existing deep networks and report their results in two ways: single-frame classification and video classification (see Section IV).
Ii Related Work
This section is dedicated to briefly reviewing related work on UAV image/video analysis.
Ii-a Object Detection
have been made to seek out useful human-designed visual features such as texture feature, color feature, and histogram of oriented gradients (HOG), or their combinations to represent objects. Recently, deep convolutional neural networks (CNNs) have been harnessed for detecting objects in UAV data and shown promising results[40, 41]. For examples,  proposes an R-Net that uses rotatable bounding boxes to simultaneously localize vehicles and identify their orientations in UAV images and videos. In , the authors study how to train CNNs effectively on a substantially imbalanced dataset for the purpose of detecting mammals in UAV images. To detect objects of different scales,  devises a hierarchical selective filtering layer and introduces it into Faster R-CNN  architecture for ship detection in UAV and satellite images. Several works [45, 46, 47] focus on detecting and counting people in complex crowd and outdoor scenes from overhead images. In addition, there have recently been numerous datasets aiming at object detection from UAV data, e.g., UAVDT  and VisDrone .
Tracking objects in aerial videos is another promising topic. In , the authors make use of a correlation filter-based online learning framework to track single objects, e.g., pedestrian, vehicle, and building, in UAV videos. From single object tracking to multi-object tracking,  views the latter task as a voting problem of trajectories from an optical flow field and achieves satisfactory multi-object tracking results in aerial videos. For real-time UAV applications, the authors of  present an onboard long-term object tracking algorithm that uses a reliable global-local object model and achieve real-time tracking performance. As to publicly available datasets in this direction, in , the authors render real-world scenarios and a wide range of moving objects commonly seen in aerial videos by the use of a photo-realistic simulator (Unreal Engine 4). Finally, they produce 123 videos with more than 110K video frames, which can be used to train models for long-term aerial tracking.  builds a benchmark of high diversity, consisting of 70 videos captured by UAVs, for object tracking tasks. Furthermore, the aforementioned two object detection datasets, i.e., UAVDT  and VisDrone , also provide annotations for tracking.
Ii-C Semantic Segmentation
Semantic segmentation of UAV data refers to a process that each pixel in an image or video is assigned a category label. This task has received much attention recently, as it enables drones to perceive complex scenes. Several datasets, e.g., ICG-DroneDataset444http://dronedataset.icg.tugraz.at/, AeroScapes , UAVid , and Skyscapes , have been proposed to facilitate progress in this direction. Methodological research in this field still remains underexplored.
Ii-D Beyond Perception Towards Content Understanding
The abovementioned perception tasks, namely object detection, tracking, and semantic segmentation, can answer the following question: what objects appear in a scene and where are they located? However, the visual understanding of UAV data goes well beyond this. With just one glance at a video clip, we are capable of imagining the world behind pixels: for example, we can infer what happens and what may happen next, i.e., recognize events, activities, and actions. While this task is effortless for humans, it is enormously hard for vision algorithms. In this direction, a few pioneer works can be found in [58, 59], but these studies are conducted in well-controlled environments.
Iii The ERA Dataset
This work aims to devise an aerial video dataset which covers an extensive range of events. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales.
Iii-a Collection and Annotation
We start by creating our taxonomy (cf. Fig. 3) by building a set of the 24 most commonly seen events from aerial scenes. The event categories refer to the Wikipedia. Moreover, to investigate if models can distinguish events from normal videos, we set up a category called non-event, which comprises videos not including specific events. The 25 classes of our dataset are as follows: post-earthquake, flood, fire, landslide, mudslide, traffic collision, traffic congestion, harvesting, ploughing, constructing, police chase, conflict, baseball, basketball, boating, cycling, running, soccer, swimming, car racing, party, concert, parade/protest, religious activity, and non-event. Fig. 2 shows the overview of our ERA dataset.
In order to collect candidate videos for further labeling, we search YouTube by parsing the metadata of videos and crawling the search engine to create a collection of candidate videos for each category. Then we download all videos and send them to data annotators. Each annotator is asked to localize 5-second video snippets that depict specific events and cut them from long candidate videos. To improve the efficiency of this procedure, we make use of a toolbox. Fig. 4 shows the user interface of this toolbox.
There are three different annotators with payments who are responsible for validating cut videos during the annotation procedure. The first annotator generates primary annotations to begin with. Then annotations from the first round are sent to the second annotator for tune-ups. Finally the third annotator screen all generated 5-second videos and remove videos with quite similar contents. Overall, the total annotation time is around 290 hours.
Iii-B Dataset Statistics
The goal of this work is to collect a large, diverse dataset that can be used to train models for event recognition in UAV videos. As we gather aerial videos from YouTube, the largest video sharing platform in the world, we are capable of including a large breadth of diversity (see Fig. 5), which is more challenging than making use of self-collected data [58, 59]. In total, we have gathered and annotated 2,866 videos for 25 classes. Each video sequence is at 24 fps (frames per second), in 5 seconds, and with a spatial size of 640640 pixels. The train/test split can be found in Fig. 6 and Section IV-A. Fig. 6 exhibits the distribution of all classes. The red and blue bars represent the numbers of training and test samples in each category, respectively, and green bars denote the total number of instances in each category.
To build a diverse dataset, we collect not only high quality UAV videos but also ones acquired in extreme conditions. By doing so, many challenging issues of event recognition in overhead videos in real-world scenarios, e.g., low spatial resolution, extreme illumination conditions, and bad weathers, can be investigated. True aerial video parsing methods should be capable of recognizing events under such extreme conditions.
|Dataset||Type of Task||Data Source||Video||# Classes||# Samples||Year|
|UCLA Aerial Event dataset ||human-centric event recognition||self-collected (actor staged)||✓||12||104||2015|
|Okutama-Action dataset ||human action detection||self-collected (actor staged)||✓||12||-||2017|
|AIDER dataset ||disaster event recognition||Internet||✗||5||2,545||2019|
|ERA dataset (Ours)||general event recognition||YouTube||✓||25||2,866||2020|
Iii-C Comparison with Other Aerial Data Understanding Datasets
The first significant effort to build a standard dataset for aerial video content understanding can be found in , in which the authors make use of a GoPro-equipped drone to collect video data at an altitude of 25 meters in a controlled environment to build a dataset called UCLA Aerial Event dataset. There are about 15 actors involved in each video. This dataset includes two different sites at a park in Los Angeles, USA and 104 event instances that present 12 classes related to human-human and human-object interactions. The events are: exchange box, play frisbee, info consult, pick up, queue for vending machine, group tour, throw trash, sit on table, picnic, serve table, sell BBQ, and inspection hide.
The authors of  propose Okutama-Action dataset for understanding human actions from a bird’s eye view. Two UAVs (DJI Phantom 4) with a flying altitude of 10-45 meters above the ground are used to capture data, and all videos included in this dataset are gathered at a baseball field in Okutama, Japan. There are 12 actions, including handshaking, hugging, reading, drinking, pushing/pulling, carrying, calling, running, walking, lying, sitting, and standing.
In , the authors build an aerial image dataset, termed as AIDER, for emergency response applications. This dataset only involves four disaster events, namely fire/smoke, flood, collapsed building/rubble, and traffic accident, and a normal case. There are totally 2,545 images collected from multiple sources, e.g., Google/Bing Images, websites of news agencies, and YouTube.
Both the UCLA Aerial Event dataset  and the Okutama-Action dataset  are small in today’s terms for aerial video understanding, and their data are gathered in well-controlled environments and only focus on several human-centric events. And the AIDER dataset  is an image dataset with only 5 classes for disaster event classification. In contrast, our ERA is a relatively large-scale UAV video content understanding dataset, aiming to recognize generic dynamic events from an aerial view. A comprehensive overview of these most important comparable datasets and their features is given in Table I.
The proposed ERA dataset poses the following challenges:
Although the ERA dataset is the largest dataset for event recognization in aerial videos yet, its size is still relatively limited as compared to video classification datasets in computer vision. Hence there exists the small data challenge in the model training.
The imbalanced distribution across different classes (cf. Fig. 6) brings a challenge of learning unbiased models on an imbalanced dataset.
In this dataset, events happen in various environments and are observed at different scales, which leads to a significant intra-class variation and inter-class similarity (cf. Fig. 5).
Iv-a Experimental Setup
Data. As to the split of training and test sets, we obey the following two rules: 1) videos cut from the same long video are assigned to the same set, and 2) the numbers of training and test videos per class are supposed to be nearly equivalent. Because video snippets stemming from the same long video usually share similar properties (e.g., background, illumination, and resolution), this split strategy is able to evaluate the generalization ability of a model. The statistics of training and test samples are exhibited in Fig. 6. During the training phase, 10% of training instances are randomly selected as the validation set.
To compare models comprehensively, we make use of per-class precision and overall accuracy as evaluation metrics. More specifically, the per-class precision is calculated by the following equation:
where true positives and false positives are measured with respect to each class. The overall accuracy is calculated by counting the number of correctly predicted samples and normalizing that number by the total number of test samples. Here, we mainly consider the latter metric as it comprehensively indicates the classification performance of a model on all classes.
Iv-B Baselines for Event Classification
Single-frame classification models. We first describe single-frame classification models where only a single video frame is selected (the middle frame in this paper) from a video as the input to networks. We summarize the used single-frame models as follows.
. VGGNet is mainly composed of five convolutional blocks and three fully-connected layers, and each block contains several convolutional layers and one max-pooling layer. The size of all convolutional filters is. We train a 16-layer VGGNet (VGG-16) and a 19-layer VGGNet (VGG-19) in our experiments.
Inception networks [62, 63, 64, 65]. Inception models aim to learn miscellaneous features by leveraging convolutional filters of various sizes simultaneously. The naive version of an Inception model consists of , , and convolutions as well as a max pooling. However, the computational cost of the model is boosted with the increasing number of filters/units, especially those in deep layers. To tackle this problem, convolutions are introduced to improve computational efficiency. Here we employ Inception-v3  to perform single-frame classification.
ResNet . ResNet is characterized by explicitly learning residual mapping functions via shortcut connections. In contrast to plain networks, such residual learning architecture can well address the degradation problem and enable networks to go deeper. In our experiments, we make use of three variations: a 50-layer ResNet (ResNet-50), a 101-layer ResNet (ResNet-101), and a 152-layer ResNet (ResNet-152).
MobileNet . MobileNet is a light-weight CNN, and it facilitates the utilization of deep neural networks in source restricted applications, e.g., mobile and embedded vision applications. To reduce computational cost and model size, MobileNet employs depth separable convolutions, which are implemented by factorizing standard convolutions into depthwise and pointwise convolutions. Besides, two hyper-parameters, width multiplier and resolution multiplier , are defined to further shrink the network. Specifically, the former is responsible for squashing input channels of each layer, while the latter reduces the resolution of input images. Here, we set them by default: and .
DenseNet . DenseNet maximizes information flow between various layers by directly connecting all layers of equivalently-sized feature maps with each other. In addition, concatenation instead of element-wise addition is utilized to combine features from early and later layers so that information can be preserved. With this design, all feature maps are taken into consideration for making final predictions. To thoroughly explore the performance of DenseNet, we experiment with a 121-layer DenseNet (DenseNet-121), a 169-layer DenseNet (DenseNet-169), and a 201-layer DenseNet (DenseNet-201).
NASNet . NASNet refers to network architectures that are learned on datasets of interest using a neural architecture search (NAS) framework . This framework is designed to search for the best-performing network in a predefined search space. However, directly searching on a large dataset is expensive, and thus, authors in  propose to search for a transferable architecture on a small dataset, which can then be transferred to a relatively large dataset. In our experiments, we select a NASNet model that is searched on the CIFAR-10 dataset and achieves the best performance on the ImageNet dataset, namely NASNet-L, to perform single-frame classification.
Video classification models. These models take several video frames as input, so that they can learn temporal information from videos.
C3D . C3D (3D convolutional network) aims to extract spatiotemporal features with 3D convolutional filters and pooling layers. Compared to conventional 2D CNNs, 3D convolutions and pooling operations in C3D can preserve the temporal information of input signals and model motion as well as appearance simultaneously. Moreover, authors in  demonstrate that the optimal size of 3D convolutional filters is 333. In our experiments, we train two C3D555https://github.com/tqvinhcs/C3D-tensorflow networks with pre-trained weights on the Sport1M dataset  and the UCF101 dataset  (see C3D and C3D in Table III), respectively.
P3D ResNet . P3D ResNet (pseudo-3D residual network) is composed of pseudo-3D convolutions, where conventional 3D convolutions are decoupled into 2D and 1D convolutions in order to learn spatial and temporal information separately. With such convolutions, the model size of a network can be significantly reduced, and the utilization of pre-trained 2D CNNs is feasible. Besides, inspired by the success of ResNet , P3D ResNet employs ResNet-like architectures to learn residuals in both spatial and temporal domains. In our experiments, we train two 199-layer P3D ResNet666https://github.com/zzy123abc/p3d (P3D-ResNet-199) models with pre-trained weights on the Kinetics dataset  and the Kinetics-600 dataset  (see P3D-ResNet-199 and P3D-ResNet-199 in Table III), respectively.
I3D . I3D (inflated 3D ConvNet) expands 2D convolution and pooling filters to 3D, which are then initialized with inflated pre-trained models. Particularly, weights of 2D networks pre-trained on the ImageNet dataset are replicated along the temporal dimension. With this design, not only 2D network architectures but also pre-trained 2D models can be efficiently employed to increase the learning efficiency and performance of 3D networks. To assess the performance of I3D on our dataset, we train two I3D777https://github.com/LossNAN/I3D-Tensorflow models whose backbones are both Inception-v1  (I3D-Inception-v1) with pre-trained weights on the Kinetics dataset  and Kinetics+ImageNet, respectively (see I3D-Inception-v1 and I3D-Inception-v1 in Table III).
TRN . Temporal relation network (TRN) is proposed to recognize human actions by reasoning about multi-scale temporal relations among video frames. By leveraging the proposed plug-and-play relational reasoning module, TRN can even accurately predict human gestures and human-object interactions through sparsely sampled frames. For our experiments, we train TRNs888https://github.com/metalbubble/TRN-pytorch with 16 multi-scale relations and select the Inception architecture as the backbone. Notably, we experiment two variants of the Inception architecture: BNInception  and Inception-v3 . We initialize the former with weights pre-trained on the Something-Something V2 dataset  (TRN-BNInception in Table III) and the latter with weights pre-trained on the Moments in Time dataset  (TRN-Inception-v3 in Table III).
Iv-C Baseline Results
Quantitative results of single-frame classification models and video classification models are reported in Table II and Table III, respectively. As we can see, DenseNet-201 achieves the best performance, an OA of 62.3%, in the single-frame classification task and marginally surpasses the second best model, Inception-v3, by 0.2%. For the video classification task, TRN-Inception-v3 performs superiorly and gains an OA of 64.3%. By comparing Table II and Table III, it is interesting to observe that the best-performed video classification model obtains the highest OA, which demonstrates the significance of exploiting temporal cues in event recognition from aerial videos.
We further show some predictions of the best two single-frame classification network architectures (i.e., Inception-v3 and DenseNet-201) and the best two video classification network architectures (i.e., I3D-Inception-v1 and TRN-Inception-v3) in Fig. 7
. As shown in the first row, frames/videos with discriminative event-relevant characteristics, such as collapsed buildings, congested traffic states on a highway, and smoke rising from a residential area, can be accurately recognized by all baselines with high confidence scores. Besides, high-scoring predictions of TRN in identifying parade/protest and concert illustrate that efficiently exploiting temporal information helps in distinguishing events of minor inter-class variances. Moreover, we observe that extreme conditions might disturb predictions, for instance, frames/videos of night and snow scenes (see Fig.7) tend to be misclassified.
Despite successes achieved by these baselines, there are still some challenging cases as shown in Fig. 8. A common characteristic shared by these examples is that event-relevant attributes such as human actions are not easy to recognize, and this results in failures to identify these events. To summarize, event recognition in aerial videos is still a big challenge, and may benefit from better recognizing discriminative attributes as well as exploiting temporal cues.
In Fig. 9, we provide confusion matrices of the best single-frame classification model DenseNet-201 and the best video classification model TRN-Inception-v3.
We present ERA, a dataset for comprehensively recognizing events in the wild form UAV videos. Organized in a rich semantic taxonomy, the ERA dataset covers a wide range of events involving diverse environments and scales. We report results of plenty of deep networks in two ways: single-frame classification and video classification. The experimental results show that this is a hard task for the remote sensing field, and the proposed dataset serves as a new challenge to develop models that can understand what happens on the planet from an aerial view.
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