Revisiting Temporal Modeling for Video-based Person ReID

Video-based person reID is an important task, which has received much attention in recent years due to the increasing demand in surveillance and camera networks. A typical video-based person reID system consists of three parts: an image-level feature extractor (e.g. CNN), a temporal modeling method to aggregate temporal features and a loss function. Although many methods on temporal modeling have been proposed, it is hard to directly compare these methods, because the choice of feature extractor and loss function also have a large impact on the final performance. We comprehensively study and compare four different temporal modeling methods (temporal pooling, temporal attention, RNN and 3D convnets) for video-based person reID. We also propose a new attention generation network which adopts temporal convolution to extract temporal information among frames. The evaluation is done on the MARS dataset, and our methods outperform state-of-the-art methods by a large margin. Our source codes are released at this https URL.

[1]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Bingbing Ni,et al.  Person Re-identification via Recurrent Feature Aggregation , 2016, ECCV.

[3]  Xiaogang Wang,et al.  Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Ramakant Nevatia,et al.  RED: Reinforced Encoder-Decoder Networks for Action Anticipation , 2017, BMVC.

[5]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[6]  Jingdong Wang,et al.  Deeply-Learned Part-Aligned Representations for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Shiliang Zhang,et al.  Pose-Driven Deep Convolutional Model for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Yu Liu,et al.  Quality Aware Network for Set to Set Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Edward J. Delp,et al.  A Two Stream Siamese Convolutional Neural Network for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Ramakant Nevatia,et al.  Cascaded Boundary Regression for Temporal Action Detection , 2017, BMVC.

[14]  Zhen Zhou,et al.  See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yu Cheng,et al.  Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Shih-Fu Chang,et al.  Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Fabio Viola,et al.  The Kinetics Human Action Video Dataset , 2017, ArXiv.

[18]  Jesús Martínez del Rincón,et al.  Recurrent Convolutional Network for Video-Based Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Kaiqi Huang,et al.  Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  R. Nevatia,et al.  TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[22]  Yutaka Satoh,et al.  Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Lucas Beyer,et al.  In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.