A Multiple Layers Re-ranking Method for Person Re-identification

Pedestrian re-identification (re-ID) is a video surveillance technology for specific pedestrians in non-overlapping multi-camera scenes. However, due to the influence of dramatic changes in perspectives and pedestrian occasions, it is still a huge challenge to find a stable, reliable algorithm in high accuracy rate. In this paper, a multiple layers re-ranking approach is proposed to jointly account for that challenge. The re-ID is viewed as a multiple metrics ranking and optimizing problem by using a Multiple Layers Re-ranking framework. In this paper, multiple metrics are proposed by employing the correlation of different features to exploit comprehensive complementary information. Based on them, a multiple layers re-ranking framework is constructed to optimize and re-rank the initial results, which is more stable and effective than a single metric. Besides, a high similarity set is proposed to reduce the interference of appearance visual ambiguous samples. Through it, more effective candidate samples are selected into the re-ranking framework, improving the robustness. Experimental results on four person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, the matching rate of rank-1, our method outperforms the state-of-the-art methods on these datasets. Our code is available https://github.com/gengshuze/MLRL_re-id.git

[1]  Alessandro Perina,et al.  Multiple-Shot Person Re-identification by HPE Signature , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Truong Q. Nguyen,et al.  Relevance Subject Machine: A Novel Person Re-identification Framework , 2017, ArXiv.

[3]  Bingpeng Ma,et al.  Covariance descriptor based on bio-inspired features for person re-identification and face verification , 2014, Image Vis. Comput..

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

[5]  Xiaogang Wang,et al.  Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Wen Gao,et al.  Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Shengcai Liao,et al.  Salient Color Names for Person Re-identification , 2014, ECCV.

[8]  David Zhang,et al.  Maximal granularity structure and generalized multi-view discriminant analysis for person re-identification , 2018, Pattern Recognit..

[9]  Alberto Del Bimbo,et al.  3D facial expression recognition using SIFT descriptors of automatically detected keypoints , 2011, The Visual Computer.

[10]  Qi Tian,et al.  Enhancing Person Re-identification in a Self-Trained Subspace , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[11]  Shaogang Gong,et al.  Learning a Discriminative Null Space for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  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).

[13]  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).

[14]  Yongjie Chu,et al.  Multiple feature subspaces analysis for single sample per person face recognition , 2017, The Visual Computer.

[15]  Shengcai Liao,et al.  Learning Efficient Image Representation for Person Re-Identification , 2017, ArXiv.

[16]  Sergio A. Velastin,et al.  Local Fisher Discriminant Analysis for Pedestrian Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Ziyan Wu,et al.  A Comprehensive Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets , 2016, ArXiv.

[18]  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.

[19]  Huimin Yu,et al.  Adaptive Metric Learning and Probe-Specific Reranking for Person Reidentification , 2017, IEEE Signal Processing Letters.

[20]  Jian-Huang Lai,et al.  An Asymmetric Distance Model for Cross-View Feature Mapping in Person Reidentification , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Xiaogang Wang,et al.  Person Re-Identification by Saliency Learning , 2014 .

[22]  Alberto Del Bimbo,et al.  Multichannel-Kernel Canonical Correlation Analysis for Cross-View Person Reidentification , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[23]  Fei Xiong,et al.  Person Re-Identification Using Kernel-Based Metric Learning Methods , 2014, ECCV.

[24]  Slawomir Bak,et al.  Learning to Match Appearances by Correlations in a Covariance Metric Space , 2012, ECCV.

[25]  Yimin Wang,et al.  Person re-identification with content and context re-ranking , 2015, Multimedia Tools and Applications.

[26]  Honggang Zhang,et al.  Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[27]  Sameh Khamis,et al.  Person re-identification using semantic color names and RankBoost , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[28]  Alberto Del Bimbo,et al.  Person Re-Identification by Iterative Re-Weighted Sparse Ranking , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Shin'ichi Satoh,et al.  Person Reidentification via Discrepancy Matrix and Matrix Metric , 2018, IEEE Transactions on Cybernetics.

[30]  Zheng Huang,et al.  Person Re-Identification by Weighted Integration of Sparse and Collaborative Representation , 2017, IEEE Access.

[31]  Takahiro Okabe,et al.  Hierarchical Gaussian Descriptor for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[33]  Shaogang Gong,et al.  Reidentification by Relative Distance Comparison , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[36]  Jian Cheng,et al.  Person Re-Identification Based on Kernel Large Margin Nearest Neighbor Classification , 2016, ICC 2016.