Hierarchical Refined Local Associations for Robust Person Re-Identification

Person re-identification is the process to identify a person from images/ videos captured from different nonoverlapping cameras in an autonomous way. The biological vision scheme emphasis on local discriminative cues in addition to the global appearance of a person for re-identification. The local cues are helpful to identify a person even if viewed at different scales and with different backgrounds. To emphasize on local cues, in this paper we present a refined association scheme for local parts of the images. The proposed scheme eliminates the effects of scale differences and background noise for automated person re-identification. Our approach divides the image of a person in horizontal strips and vertical sub-patches. A hierarchical refined associations based network (HRAN) is introduced to establish the refined associations among local segments of given images. In the first phase, the associations are established among horizontal strips of two images. In the next phase, the vertical sub-patches of associated horizontal strips are aligned/ linked with each other. Background noise and scale differences between images are addressed effectively using the proposed two-step mechanism. The triplet loss is used to optimize the refined local associations among images. A different weighting scheme is used for local and global losses for optimization of proposed model. The evaluation results of proposed methodology on two publicly available large scale datasets Market-1501 and DukeMTMC-ReID verified the effectiveness of proposed refined alignment method.

[1]  Muhammad Moazam Fraz,et al.  VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture , 2019, Pattern Recognit..

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

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

[4]  Shishir K. Shah,et al.  A survey of approaches and trends in person re-identification , 2014, Image Vis. Comput..

[5]  Haiqing Li,et al.  Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Syed Farooq Ali,et al.  On the frontiers of pose invariant face recognition: a review , 2019, Artificial Intelligence Review.

[7]  Yi Yang,et al.  Camera Style Adaptation for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

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

[10]  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).

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

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

[13]  Muhammad Moazam Fraz,et al.  DUPL-VR: Deep Unsupervised Progressive Learning for Vehicle Re-Identification , 2018, ISVC.

[14]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[17]  Jian Sun,et al.  AlignedReID: Surpassing Human-Level Performance in Person Re-Identification , 2017, ArXiv.

[18]  Shengcai Liao,et al.  Deep Metric Learning for Person Re-identification , 2014, 2014 22nd International Conference on Pattern Recognition.

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

[20]  Carlo Tomasi,et al.  Features for Multi-target Multi-camera Tracking and Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Octavia I. Camps,et al.  DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

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

[24]  Muhammad Moazam Fraz,et al.  Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning , 2018, IEEE Access.

[25]  Muhammad Moazam Fraz,et al.  Weighted hybrid features for person re-identification , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).

[26]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Saadia Batool,et al.  End to End Person Re-Identification for Automated Visual Surveillance , 2018, 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS).

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

[29]  Soon Ki Jung,et al.  Two Stream Deep CNN-RNN Attentive Pooling Architecture for Video-Based Person Re-identification , 2018, CIARP.

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

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

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