Improving Person Re-identification via Pose-Aware Multi-shot Matching

Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person reidentification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and efficiently conducts multi-shot matching based on the target pose information. Experimental results using public person reidentification datasets show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.

[1]  Yang Li,et al.  Multi-Shot Human Re-Identification Using Adaptive Fisher Discriminant Analysis , 2015, BMVC.

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

[3]  Xiaogang Wang,et al.  Locally Aligned Feature Transforms across Views , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[5]  Slawomir Bak,et al.  Person re-identification by pose priors , 2015, Electronic Imaging.

[6]  M. Gordan,et al.  Camera calibration using two or three vanishing points , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[7]  Ramakant Nevatia,et al.  Camera calibration from video of a walking human , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Chunxiao Liu,et al.  Person Re-identification: What Features Are Important? , 2012, ECCV Workshops.

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

[10]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[11]  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).

[12]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

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

[15]  Yang Li,et al.  Viewpoint Invariant Human Re-Identification in Camera Networks Using Pose Priors and Subject-Discriminative Features , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Narendra Ahuja,et al.  Pedestrian Recognition with a Learned Metric , 2010, ACCV.

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[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]  Hongdong Li,et al.  A Direct Method to Self-Calibrate a Surveillance Camera by Observing a Walking Pedestrian , 2009, 2009 Digital Image Computing: Techniques and Applications.

[20]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

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

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

[23]  Xiaogang Wang,et al.  Learning Mid-level Filters for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Rita Cucchiara,et al.  3DPeS: 3D people dataset for surveillance and forensics , 2011, J-HGBU '11.

[25]  Horst Bischof,et al.  Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.