A siamese pedestrian alignment network for person re-identification

Deep learning methods show strong ability in extracting high-level features for images in the field of person re-identification. The produced features help inherently distinguish pedestrian identities in images. However, on deep learning models over-fitting and discriminative ability of the learnt features are still challenges for person re-identification. To alleviate model over-fitting and further enhance the discriminative ability of the learnt features, we propose siamese pedestrian alignment networks (SPAN) for person re-identification. SPAN employs two streams of PAN (pedestrian alignment networks) to increase the size of network inputs over limited training samples and effectively alleviate network over-fitting in learning. In addition, a verification loss is constructed between the two PANs to adjust the relative distance of two input pedestrians of the same or different identities in the learned feature space. Experimental verification is conducted on six large person re-identification data sets and the experimental results demonstrate the effectiveness of the proposed SPAN for person re-identification.

[1]  Tao Xiang,et al.  Transferring a semantic representation for person re-identification and search , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[3]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

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

[5]  Qiang Wu,et al.  Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification , 2018, IEEE Transactions on Image Processing.

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

[7]  Hong Liu,et al.  Orientation Driven Bag of Appearances for Person Re-identification , 2016, ArXiv.

[8]  Honglak Lee,et al.  Learning to Align from Scratch , 2012, NIPS.

[9]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

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

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

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

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

[14]  Jiwen Lu,et al.  Discriminative Deep Metric Learning for Face Verification in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Jian Wang,et al.  JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images , 2019, IEEE Transactions on Information Forensics and Security.

[16]  Xuelong Li,et al.  Taking a Look at Small-Scale Pedestrians and Occluded Pedestrians , 2019, IEEE Transactions on Image Processing.

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

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

[19]  Ling Shao,et al.  Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Jianxin Wu,et al.  Person Re-Identification with Correspondence Structure Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Longhui Wei,et al.  Person Transfer GAN to Bridge Domain Gap for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[23]  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.

[24]  Nanning Zheng,et al.  Similarity learning on an explicit polynomial kernel feature map for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[26]  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).

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

[28]  End-to-End Comparative Attention Networks for Person Re-Identification. , 2017 .

[29]  Gang Wang,et al.  A Siamese Long Short-Term Memory Architecture for Human Re-identification , 2016, ECCV.

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

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

[32]  Shengcai Liao,et al.  Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[34]  M. Saquib Sarfraz,et al.  A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[35]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[36]  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).

[37]  Pong C. Yuen,et al.  Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information , 2013, 2013 IEEE International Conference on Computer Vision.

[38]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Ziyan Wu,et al.  A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  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.

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

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

[43]  Peter H. N. de With,et al.  Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment , 2012, IEEE Transactions on Consumer Electronics.

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

[45]  Kaiqi Huang,et al.  A Richly Annotated Dataset for Pedestrian Attribute Recognition , 2016, ArXiv.

[46]  Fang Liu,et al.  3D fast convex-hull-based evolutionary multiobjective optimization algorithm , 2018, Appl. Soft Comput..

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

[48]  Licheng Jiao,et al.  Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms , 2014, Inf. Sci..

[49]  Ondrej Chum,et al.  CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples , 2016, ECCV.

[50]  Qi Tian,et al.  Scalable Person Re-identification on Supervised Smoothed Manifold , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[53]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

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

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

[59]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.