Shadows appear in most natural images. Simply detecting shadows benefits many computer vision tasks such as image classification[Filippi and İnci Güneralp(2013)], image segmentation [Xu et al.(2019)Xu, Chen, Su, Ji, Xu, Memon, and Zhou] and object tracking [Saravanakumar et al.(2010)Saravanakumar, Vadivel, and Saneem Ahmed, Mohanapriya and Mahesh(2017)]
. Therefore, shadow detection has drawn a lot of interest in recent years especially with the rapid development of deep-learning-based methods[Nguyen et al.(2017)Nguyen, Yago Vicente, Zhao, Hoai, and Samaras, Zheng et al.(2019)Zheng, Qiao, Cao, and Lau, Wang et al.(2018)Wang, Li, and Yang, Ding et al.(2019)Ding, Long, Zhang, and Xiao, Le et al.(2018)Le, Vicente, Nguyen, Hoai, and Samaras]. However, recent shadow detection works mostly deal with shadows in single images while video shadow detection remains an open question despite many potential applications [Wang et al.(2020)Wang, Curless, and Seitz, Le and Samaras(2020a), Le and Samaras(2020b), Le et al.(2016)Le, Nguyen, Yu, and Samaras].
A shadow video typically consists of hundreds of frames that contain shadows varying in shapes and intensities. The detection problem is compounded by video-specific issues such as motion blur. Thus, simply applying image shadow detection methods frame-by-frame on video often yields inconsistent predictions (see Fig 1.d). Instead, a common strategy to deal with video data is to leverage temporal information across video frames [Zhu et al.(2017)Zhu, Xiong, Dai, Yuan, and Wei, Yan et al.(2019)Yan, Li, Xie, Li, Wang, Chen, and Lin]. Here the difficulty lies in how to incorporate information across video where there is spatial misalignment between frames due to the movements of objects and cameras.
In this paper, we propose a straightforward but powerful deep-learning based method to obtain a rich feature representation for video shadow detection. We focus on dealing with the temporal misalignment between frame representations. Our strategy is simple: we align the features across frames by optical flow and then linearly combine them to obtain the per-frame final feature representation. Optical flow is easy to obtain [Liu et al.(2020)Liu, Zhang, He, Liu, Wang, Tai, Luo, Wang, Li, and Huang] and can effectively align spatial content, including small details. As this warping is computationally efficient, we can apply it on multiple layers of our network.
We report that this simple optical-flow based feature aggregation scheme works surprisingly well for shadow detection in videos. We train our model in an end-to-end fashion on the Visha dataset [Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin]. Our method achieves state-of-the-art video shadow detection performance, outperforming the previous method [Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin] by a 28% BER reduction. Fig 1 illustrates the effect of our proposed temporal feature warping method. As can be seen, the features of two visually similar frames could be wildly different (column c), which results in inconsistent outputs (column d). Our method warps and then combines the features, making them consistent across frames (column e), and finally outputs stable and temporally consistent results (last column).
2 Related Work
Single image shadow detection is a well studied topic. On one hand, earlier research on image shadow detection mostly focuses on spectral or spatial features of images such as chromaticity, physical properties, geometry, and texture [Sanin et al.(2012)Sanin, Sanderson, and Lovell]. On the other hand, recent shadow detection methods show tremendous success with the rapid development of deep learning. Le et al[Le et al.(2018)Le, Vicente, Nguyen, Hoai, and Samaras] propose to train a shadow detection network together with a shadow attenuation network that generates adversarial training examples. Hu et al[Hu et al.(2018)Hu, Zhu, Fu, Qin, and Heng] propose a directional-aware feature extractor for aggregating spatial information. Zhu et al[Zhu et al.(2018)Zhu, Deng, Hu, Fu, Xu, Qin, and Heng] utilize recurrent attention residual modules to fully aggregate the global and local contexts in different layers of the CNN to detect shadows. Chen et al[Chen et al.(2020)Chen, Zhu, Wan, Wang, Feng, and Heng] further improve detection performance by introducing a multi-task mean teacher architecture which leverages unlabeled data. However, image methods trained on image datasets such as SBU [Vicente et al.(2016b)Vicente, Hou, Yu, Hoai, and Samaras] and CUHK-Shadow [Hu et al.(2021)Hu, Wang, Fu, Jiang, Wang, and Heng] do not generalize well to videos due to the lack of temporal consistency.
Video shadow detection is a classic problem on its own. Earlier work [Jacques et al.(2005)Jacques, Jung, and Musse, Shi and Liu(2019)] focuses on the spectral and spatial features, which depend heavily on the quality of data. Without temporal constraints, these methods often output inconsistent predictions across frames. The first large-scale video shadow detection dataset was proposed by Chen et al[Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin]. The dataset contains 120 fully-annotated videos with a total of 11,685 frames. They also proposed the first deep-learning-based method for video shadow detection in which a dual gated co-attention module is used to focus on common high-level features between frames. This co-attention module allows their network to filter out temporally inconsistent information from each frame representation to obtain more stable and consistent results. However, this mechanism causes the method to be less sensitive to shadow areas that substantially change across frames due to temporal misalignment. By contrast, our method performs temporal alignment before combining features. Besides, our temporal alignment module can be used for all layers of the network, allowing us to pick up even small shadow areas. Co-attention can only be applied on high-level feature maps due to its computational cost.
Optical flow is used in various high-level video tasks [Shin et al.(2005)Shin, Kim, Kang, Lee, Paik, Abidi, and Abidi, Zhong et al.(2013)Zhong, Liu, Ren, Zhang, and Ren, Buades et al.(2016)Buades, Lisani, and Miladinović]. Recent deep learning based methods [Ilg et al.(2017)Ilg, Mayer, Saikia, Keuper, Dosovitskiy, and Brox, Sun et al.(2018)Sun, Yang, Liu, and Kautz]
for optical flow estimation are fairly accurate and efficient in inference. However, most popular datasets used in training optical flow estimation models,e.g, MPI Sintel [Butler et al.(2012)Butler, Wulff, Stanley, and Black] and KITTI 2015 [Menze and Geiger(2015)], are sufficiently different from shadow detection datasets [Wang et al.(2018)Wang, Li, and Yang, Vicente et al.(2016a)Vicente, Hoai, and Samaras, Hu et al.(2021)Hu, Wang, Fu, Jiang, Wang, and Heng]. To compensate for this domain shift, we use a simple module to refine optical flow before using it to warp our features. Liu et al[Liu et al.(2020)Liu, Zhang, He, Liu, Wang, Tai, Luo, Wang, Li, and Huang] propose an unsupervised dense optical flow estimation network with better cross dataset generalization capability by learning from abundant augmentations of training data.
The overall structure of our framework is illustrated in Fig 2. Our model consists of two branches with identical architectures. The input of our model is a RGB video frame pair. The two images are input to the two branches of the model to extract two sets of feature maps across the three different layers of the network. Throughout the network, these features are progressively enriched by the information from the features of the other image via temporal warping and linear combination.
In particular, we first obtain two dense optical flow fields from the two images using ARFlow [Liu et al.(2020)Liu, Zhang, He, Liu, Wang, Tai, Luo, Wang, Li, and Huang]. Following [Gadde et al.(2017)Gadde, Jampani, and Gehler, Li et al.(2021)Li, Zhao, He, Zhu, and Liu], we train a small module to refine these optical flow fields to better suit the feature warping task, depicted as FlowCNN in Fig 2. At multiple layers of the network, we combine the features of each branch with the aligned features from the other. A flow-guided warp (FGwarp) module is used to first warp the frame feature representation to spatially align it with the content of the other frame and then linearly combine the original features with the warped features from the other frame. This simple feature aggregation scheme ensures consistency between the two frame representations. The combined features of different levels are input through a shadow refinement module to generate the final shadow mask prediction.
3.2 Shadow detector network
Our network consists of two branches with identical architecture. For each branch, we use the MobileNet V2 [Sandler et al.(2018)Sandler, Howard, Zhu, Zhmoginov, and Chen] as the backbone feature extractor. Each branch consists of a series of inverted residual bottlenecks (IRBs) [Sandler et al.(2018)Sandler, Howard, Zhu, Zhmoginov, and Chen]. We input the feature maps of the , the and the last block of each branch, which encode the low, mid, and high-level features respectively, to the FGwarp module to obtain the corresponding combined features. Finally, these three combined feature maps are input to a detail enhancement module [Hu et al.(2021)Hu, Wang, Fu, Jiang, Wang, and Heng] to refine the features and predict the final shadow mask.
3.3 Optical flow estimation
We use a pre-trained ARFlow [Liu et al.(2020)Liu, Zhang, He, Liu, Wang, Tai, Luo, Wang, Li, and Huang] model to produce optical flow for our two input frames. This network is trained on MPI Sintel [Butler et al.(2012)Butler, Wulff, Stanley, and Black]
which differs in object types and occlusions from our shadow video data. Thus, we train a flow refinement module, FlowCNN, to refine (by adapting the domain) the output of ARFlow to better fit the feature warping task. The input for the FlowCNN consists of the optical flow from ARFlow, the two input frames, and the pixel-wise difference of the two frames. The network consists of 4 convolutional layers, in which the first two layers are followed by a BatchNorm and a ReLU layer. The output of the third layer is then concatenated with the original flow and passed to the last convolution layer to obtain the refined optical flow. We train our proposed FlowCNN together with the shadow detector network in an end-to-end manner.
3.4 Flow warping and combination
We enforce consistency between frame feature representations by mutually exchanging their intermediate features. Since motion in video causes the spatial misalignment between the content of consecutive frames, we first need to apply an optical flow based feature warping to align the features. A flow-guided warp (FGwarp) module is defined for the task. Fig 3 illustrates this scheme for transferring features from frame to frame .
Given a pair of feature maps, and , and the refined optical flow . The feature can be warped to spatially align with the contents of . We perform this feature warping for each channel of the feature map separately. The value at a pixel , channel of the warped feature can be computed as follows:
where enumerates all spatial locations in the feature map and
denotes the bi-linear interpolation kernel.
Given the refined optical flow and the feature map , we can propagate the features from the frame to get the aligned :
The function is implemented by applying Eq.1 on different feature map levels. Then, the combined feature map can be computed as follows:
where and represent the per-channel coefficients of the linear combination that combines the two features, represents channel-wise scalar multiplication. The resulting is then passed to the following layers in the feature extractor backbone.
3.5 Training and Inference
while other components are trained from scratch. We use stochastic gradient descent (SGD) with momentum ofand weight decay of to optimize the whole network. The training is in an end-to-end fashion where the objective function is to minimize the mean squared error (MSE) between the ground-truth and predicted shadow masks. The initial learning rate is set to , updated by poly strategy [Liu et al.(2015)Liu, Rabinovich, and Berg] with the power of . We train the model for k iterations. All input frames are resized to
. The coefficient vectorsand are initialized as and , i.e., the temporal warping is not enforced at start. is set to 1 in training, i.e., adjacent frames are used as input pairs to train our model.
For inference, we resize the inputs to . We predict shadow masks for each pair of adjacent frames. Thus, each frame, except the first and last one, will be used in two different inference passes. The final shadow mask of each frame is the average of the frame’s two output shadow masks.
4 Experiments and Results
4.1 Evaluation datasets and metrics
Benchmark dataset We use the ViSha dataset [Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin] to evaluate our proposed method. The ViSha dataset has 4788 frames from 50 videos for training and 6897 frames from 70 videos for testing. All methods are trained on the training set and evaluated on the testing set for a fair comparison.
Evaluation metric We employ the commonly-used balanced error rate (BER) to evaluate shadow detection performance, which is defined as: BER , where are the total numbers of true positive, true negative, false positive, and false negative pixels respectively. Since shadow pixels are usually minority in natural images, the BER is less biased than mean pixel accuracy. In general, lower BER indicates better shadow detection performance. We also provide separate mean pixel error rates for the shadow and non-shadow classes.
4.2 Comparison with the state-of-the-art
There is only one CNN-based video shadow detection method,TVSD [Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin]. Besides, we compare our method with state-of-the-art image shadow detection methods including BDRAR [Zhu et al.(2018)Zhu, Deng, Hu, Fu, Xu, Qin, and Heng], MTMT [Chen et al.(2020)Chen, Zhu, Wan, Wang, Feng, and Heng] and FSDNet [Hu et al.(2021)Hu, Wang, Fu, Jiang, Wang, and Heng]. We obtain the results of TVSD from their provided pre-trained model. We train BDRAR, MTMT, and FSDNet on the Visha dataset using their official source-codes with their default settings.
|BDRAR[Zhu et al.(2018)Zhu, Deng, Hu, Fu, Xu, Qin, and Heng]||13.34||20.80||5.89|
|MTMT[Chen et al.(2020)Chen, Zhu, Wan, Wang, Feng, and Heng]||14.55||21.15||7.94|
|FSDNet[Hu et al.(2021)Hu, Wang, Fu, Jiang, Wang, and Heng]||14.59||18.88||10.30|
|TVSD[Chen et al.(2021)Chen, Wan, Zhu, Shen, Fu, Liu, and Qin]||16.76||31.78||1.75|
4.2.1 Quantitative results
Table 1 reports the performance of all methods on the ViSha dataset. Our method performs best on overall BER, we obtain a 10% error reduction and a 18% error reduction compared with BDRAR and FSDNet, respectively. TVSD has the best performance on non-shadow error. However, this is mainly because the method is insensitive to shadow areas, especially the fast changing shadows and the shadows with small areas. For the shadow areas, our method achieves the lowest error rate. The results show that leveraging temporal information on intermediate representations can boost the shadow detection performance for videos significantly.
4.2.2 Qualitative results
In Fig 4, we show some shadow detection results of our method in comparison with other methods. In the first row, we can see that all methods produce fairly accurate shadow masks with clear boundaries between shadow and non-shadow areas. Comparing with FSDNet, our method generates shadow masks with smoother boundaries without disjoint areas. Comparing with TVSD, our method is able to capture more shadow details, as shown in the last two rows. Fig 5 shows the performance of our method on three consecutive video frames of two example videos. As can be seen, TVSD is not robust in detecting moving shadows and not sensitive to small shadow areas. By contrast, our method predicts more accurate shadow masks in all cases. As we can see in the right three columns, only our method is able to detect the fast moving shadow regions.
4.2.3 Failure cases
Some failure cases of our method are shown in Fig 6
. Many of them are caused by dark objects being incorrectly classified as shadows. Dark objects are naturally difficult for shadow detection, especially when the dark object is the majority of scene. Correctly classifying these cases requires contextual understanding of the scenes as well as sufficient training data. Note that the training set of the Visha dataset has only 50 videos. Improving the generalization of models trained on such small-sized dataset is challenging.
4.3 Application on Video Shadow Removal
Shadow masks can be further used to remove shadows to create shadow-free images. We test our predicted shadow masks on the model proposed in [Le and Samaras(2019), Le and Samaras(2020b)] which takes the original image and the shadow mask as input and produces the shadow-free version of the image. Fig 7 shows the performance of using our predicted shadow mask and using the ground truth shadow mask, results show that our video shadow detection method gets reasonable results and can be used downstream to create shadow-free videos.
Large-scale video shadow detection is still in an early stage. In this paper, we show that a basic and simple idea can be extremely effective for the task. We deployed an optical-flow based feature warping and combination scheme to enforce correspondence between representations. Highly correlated intermediate representations led to an improvement of shadow prediction accuracy and consistency in the videos. In experimental results our model was able to handle large shadow appearance changes and capture small shadow regions, and outperformed the state-of-the-art methods on the existing video dataset. We believe our method will serve as an important stepping stone for future work.
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