Unidirectional information-interaction network for person re-identification

Abstract. Person re-identification (re-ID) is the task of matching the same individuals across multiple cameras, and its performance is greatly influenced by background clutter. Most re-ID methods remove background clutter using hard manners, such as the use of segmentation algorithms. However, the hard manner may damage the structure information and smoothness of original images. In this work, we propose a unidirectional information-interaction network (UI2N) that consists of a global stream (G-Stream) and a background-graying stream (BGg-Stream). The G-Stream and BGg-Stream carry out unidirectional information interaction such that their features are complementary. We further propose a soft manner with the UI2N to weaken background clutter by background-graying. The soft manner can help the UI2N filter out background interference and retain some informative background cues. Extensive evaluations demonstrate that our method significantly outperforms many state-of-the-art approaches in the challenging Market-1501, DukeMTMC-reID, and CUHK03-NP datasets.

[1]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  C. V. Jawahar,et al.  Pose-Aware Person Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Xiaogang Wang,et al.  Person Re-identification: System Design and Evaluation Overview , 2014, Person Re-Identification.

[5]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Nanning Zheng,et al.  Discriminative Feature Learning With Foreground Attention for Person Re-Identification , 2018, IEEE Transactions on Image Processing.

[7]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Yi Yang,et al.  A Discriminatively Learned CNN Embedding for Person Reidentification , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[9]  Huchuan Lu,et al.  Pose-Invariant Embedding for Deep Person Re-Identification , 2017, IEEE Transactions on Image Processing.

[10]  Shaogang Gong,et al.  Person Re-identification by Deep Learning Multi-scale Representations , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[11]  Hantao Yao,et al.  Deep Representation Learning With Part Loss for Person Re-Identification , 2017, IEEE Transactions on Image Processing.

[12]  Liang Lin,et al.  Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Yi Yang,et al.  Person Re-identification: Past, Present and Future , 2016, ArXiv.

[14]  Yinghuan Shi,et al.  GreyReID: A Novel Two-stream Deep Framework with RGB-grey Information for Person Re-identification , 2021, ACM Trans. Multim. Comput. Commun. Appl..

[15]  Qi Tian,et al.  An End-to-End Foreground-Aware Network for Person Re-Identification , 2021, IEEE Transactions on Image Processing.

[16]  Jianyuan Guo,et al.  Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[17]  Zuozhuo Dai,et al.  Batch DropBlock Network for Person Re-Identification and Beyond , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[18]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[19]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Shuicheng Yan,et al.  End-to-End Comparative Attention Networks for Person Re-Identification , 2016, IEEE Transactions on Image Processing.

[21]  Jing Xu,et al.  Attention-Aware Compositional Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Ke Gong,et al.  Feature Refinement and Filter Network for Person Re-Identification , 2021, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Tao Mei,et al.  Part-Aligned Bilinear Representations for Person Re-identification , 2018, ECCV.

[25]  Q. Tian,et al.  GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval , 2017, ACM Multimedia.

[26]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[27]  Cheng Wang,et al.  Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification , 2018, ECCV.

[28]  Yifan Sun,et al.  SVDNet for Pedestrian Retrieval , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Yi Yang,et al.  Pedestrian Alignment Network for Large-scale Person Re-Identification , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  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).

[31]  Cuiling Lan,et al.  Relation-Aware Global Attention for Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Nanning Zheng,et al.  Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Xin Ning,et al.  JWSAA: Joint weak saliency and attention aware for person re-identification , 2020, Neurocomputing.

[34]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[36]  Narendra Ahuja,et al.  Pedestrian Recognition with a Learned Metric , 2010, ACCV.

[37]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Muhittin Gokmen,et al.  Human Semantic Parsing for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[39]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Weihong Deng,et al.  Mixed High-Order Attention Network for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[41]  Shiliang Pu,et al.  Learning Incremental Triplet Margin for Person Re-identification , 2018, AAAI.

[42]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[43]  Xiong Chen,et al.  Learning Discriminative Features with Multiple Granularities for Person Re-Identification , 2018, ACM Multimedia.

[44]  Yaohua Wang,et al.  Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Liang Wang,et al.  Mask-Guided Contrastive Attention Model for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[46]  Xiaogang Wang,et al.  HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[47]  Yi Yang,et al.  Random Erasing Data Augmentation , 2017, AAAI.

[48]  Xiaogang Wang,et al.  Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[49]  Yunchao Wei,et al.  Horizontal Pyramid Matching for Person Re-identification , 2018, AAAI.

[50]  Nassir Navab,et al.  Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks , 2018, MICCAI.

[51]  Rui Yu,et al.  Divide and Fuse: A Re-ranking Approach for Person Re-identification , 2017, BMVC.

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

[53]  Shiguang Shan,et al.  Interaction-And-Aggregation Network for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Yang Yang,et al.  ABD-Net: Attentive but Diverse Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[55]  Xiaogang Wang,et al.  Eliminating Background-bias for Robust Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[56]  Lin Wu,et al.  PersonNet: Person Re-identification with Deep Convolutional Neural Networks , 2016, ArXiv.

[57]  Shaogang Gong,et al.  Person Re-Identification by Deep Joint Learning of Multi-Loss Classification , 2017, IJCAI.

[58]  Christian Poellabauer,et al.  Second-Order Non-Local Attention Networks for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[59]  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).

[60]  Ziyan Wu,et al.  Re-Identification With Consistent Attentive Siamese Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[61]  Shaogang Gong,et al.  Harmonious Attention Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[62]  Xiaogang Wang,et al.  FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification , 2018, NeurIPS.

[63]  Qi Tian,et al.  Beyond Part Models: Person Retrieval with Refined Part Pooling , 2017, ECCV.

[64]  Yinghuan Shi,et al.  MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification , 2018, ArXiv.

[65]  Jingsong Xu,et al.  Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy , 2019, 2019 IEEE Visual Communications and Image Processing (VCIP).

[66]  Tao Xiang,et al.  Multi-level Factorisation Net for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[67]  Xiaogang Wang,et al.  Person Re-Identification by Saliency Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[68]  Xu Lan,et al.  Deep Reinforcement Learning Attention Selection For Person Re-Identification , 2017, BMVC.