Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification

In recent years, deep learning (DL) has been successfully and widely applied in the person reidentification (Re-ID). However, the DL-based person Re-ID methods face a bottleneck that the scales of most existing person Re-ID databases are not large enough for training very deep models. To address this problem, a body symmetry and part-locality-guided direct nonparametric deep feature enhancement (DNDFE) method is proposed in this article. Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons, the proposed method designs two nonparametric layers, namely, the body symmetry average pooling and local normalization layers, to construct a DNDFE module to well explore the body symmetry and part locality properties. The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance. The experimental results have shown that the proposed DNDFE method is superior to multiple state-of-the-art person Re-ID methods in terms of accuracy and efficiency.

[1]  Shaogang Gong,et al.  Multi-camera activity correlation analysis , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[4]  Anurag Mittal,et al.  Deep Neural Networks with Inexact Matching for Person Re-Identification , 2016, NIPS.

[5]  Yang Hu,et al.  Cross Dataset Person Re-identification , 2014, ACCV Workshops.

[6]  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).

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

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

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

[10]  Larry S. Davis,et al.  Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[12]  Lucas Beyer,et al.  In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.

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

[14]  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).

[15]  Shengcai Liao,et al.  Embedding Deep Metric for Person Re-identification: A Study Against Large Variations , 2016, ECCV.

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

[17]  Yongzhao Zhan,et al.  Sparse representations based distributed attribute learning for person re-identification , 2017, Multimedia Tools and Applications.

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

[19]  Ming-Hsuan Yang,et al.  An Ensemble Color Model for Human Re-identification , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[20]  Xiang Li,et al.  An enhanced deep feature representation for person re-identification , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[22]  Hai Tao,et al.  Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.

[23]  François Fleuret,et al.  Scalable Metric Learning via Weighted Approximate Rank Component Analysis , 2016, ECCV.

[24]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[25]  Ling Shao,et al.  Fast Person Re-identification via Cross-Camera Semantic Binary Transformation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[28]  Liang Lin,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015, Pattern Recognit..

[29]  Yunhong Wang,et al.  Relevance Metric Learning for Person Re-Identification by Exploiting Listwise Similarities , 2015, IEEE Transactions on Image Processing.

[30]  Bingpeng Ma,et al.  Local Descriptors Encoded by Fisher Vectors for Person Re-identification , 2012, ECCV Workshops.

[31]  Kai-Kuang Ma,et al.  HOG-assisted deep feature learning for pedestrian gender recognition , 2017, J. Frankl. Inst..

[32]  Zhongfei Zhang,et al.  Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification , 2016, IJCAI.

[33]  Lei Hu,et al.  Efficient person re-identification by hybrid spatiogram and covariance descriptor , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[34]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

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

[36]  Nanning Zheng,et al.  Similarity Learning with Spatial Constraints for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Sergey Lvin,et al.  A Study of Log‐Logistic Model in Survival Analysis , 1999 .

[38]  Gang Wang,et al.  Gated Siamese Convolutional Neural Network Architecture for Human Re-identification , 2016, ECCV.

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

[40]  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.

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

[42]  Maozhen Li,et al.  Data‐driven pedestrian re‐identification based on hierarchical semantic representation , 2018, Concurr. Comput. Pract. Exp..

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

[44]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

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

[47]  Shiliang Zhang,et al.  Deep Attributes Driven Multi-Camera Person Re-identification , 2016, ECCV.

[48]  Jian-Huang Lai,et al.  Mirror Representation for Modeling View-Specific Transform in Person Re-Identification , 2015, IJCAI.

[49]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

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

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

[52]  Jian-Huang Lai,et al.  Deep Ranking for Person Re-Identification via Joint Representation Learning , 2015, IEEE Transactions on Image Processing.

[53]  Yang Hu,et al.  Exploring Structural Information and Fusing Multiple Features for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[54]  Shengcai Liao,et al.  Deep Hybrid Similarity Learning for Person Re-Identification , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[55]  Tianqi Chen,et al.  Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.

[56]  Shengcai Liao,et al.  Deep person re-identification with improved embedding and efficient training , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[57]  Wen Fang,et al.  A person re-identification algorithm based on pyramid color topology feature , 2017, Multimedia Tools and Applications.

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

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

[60]  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).

[61]  Horst Bischof,et al.  Mahalanobis Distance Learning for Person Re-identification , 2014, Person Re-Identification.

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

[63]  Lei Zhang,et al.  Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification , 2015, IEEE Transactions on Image Processing.

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

[65]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[66]  Yongzhao Zhan,et al.  AL‐DDCNN: a distributed crossing semantic gap learning for person re‐identification , 2017, Concurr. Comput. Pract. Exp..

[67]  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).

[68]  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.

[69]  Stan Z. Li,et al.  Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation , 2016, AAAI.