Person re-identification based on multi-appearance model

Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.

[1]  Zheng Wang,et al.  Region-Based Interactive Ranking Optimization for Person Re-identification , 2014, PCM.

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

[3]  Xiaogang Wang,et al.  Human Reidentification with Transferred Metric Learning , 2012, ACCV.

[4]  Shaogang Gong,et al.  Person Re-identification by Attributes , 2012, BMVC.

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

[6]  Liang Zheng,et al.  Improving Person Re-identification by Attribute and Identity Learning , 2017, Pattern Recognit..

[7]  Guillermo Sapiro,et al.  Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification , 2018, ArXiv.

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

[9]  Anton van den Hengel,et al.  Learning to rank in person re-identification with metric ensembles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Bingpeng Ma,et al.  Discriminative Image Descriptors for Person Re-identification , 2014, Person Re-Identification.

[11]  Xiaogang Wang,et al.  Joint Detection and Identification Feature Learning for Person Search , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Xin Zhao,et al.  EANet: Enhancing Alignment for Cross-Domain Person Re-identification , 2018, ArXiv.

[13]  Xiaogang Wang,et al.  Person Re-identification by Salience Matching , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Shuicheng Yan,et al.  Person Re-identification by Attribute-Assisted Clothes Appearance , 2014, Person Re-Identification.

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

[16]  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).

[17]  Yu Wu,et al.  Progressive Learning for Person Re-Identification With One Example , 2019, IEEE Transactions on Image Processing.

[18]  Alessio Del Bue,et al.  Person re-identification using sparse representation with manifold constraints , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

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

[20]  Wei-Shi Zheng,et al.  Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[23]  Zhuowen Tu,et al.  Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Kaiqi Huang,et al.  Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Shaogang Gong,et al.  Person Re-identification by Video Ranking , 2014, ECCV.

[26]  Chuan-Xian Ren,et al.  A Deep and Structured Metric Learning Method for Robust Person Re-Identification , 2019, Pattern Recognit..

[27]  Hua Yang,et al.  Multiple Scaled Person Re-Identification Framework for HD Video Surveillance Application , 2015, CCCV.

[28]  David Zhang,et al.  Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Chunxiao Liu,et al.  On-the-fly feature importance mining for person re-identification , 2014, Pattern Recognit..

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

[31]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[32]  Dan Wang,et al.  Unsupervised person re-identification with locality-constrained Earth Mover's distance , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

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

[34]  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).

[35]  Shaogang Gong,et al.  Person re-identification by probabilistic relative distance comparison , 2011, CVPR 2011.

[36]  Can Gao,et al.  Robust Color Invariant Model for Person Re-Identification , 2016, CCBR.

[37]  Francesco Solera,et al.  Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.