Person Re-Identification by Discriminative Local Features of Overlapping Stripes

The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.

[1]  Hantao Yao,et al.  Deep Representation Learning With Part Loss for Person Re-Identification , 2017, IEEE Transactions on Image Processing.

[2]  Yasar Amin,et al.  EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach , 2019, Sensors.

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

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

[5]  Lin Wu,et al.  Deep adaptive feature embedding with local sample distributions for person re-identification , 2017, Pattern Recognit..

[6]  Jonathan Loo,et al.  Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification , 2019, IEEE Access.

[7]  Huchuan Lu,et al.  Video Person Re-Identification by Temporal Residual Learning , 2018, IEEE Transactions on Image Processing.

[8]  Rui Yu,et al.  Deep-Person: Learning Discriminative Deep Features for Person Re-Identification , 2017, Pattern Recognit..

[9]  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.

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

[11]  Yang Hua,et al.  Cross-View Discriminative Feature Learning for Person Re-Identification , 2018, IEEE Transactions on Image Processing.

[12]  Hannu Tenhunen,et al.  Low-Rank Multi-Channel Features for Robust Visual Object Tracking , 2019, Symmetry.

[13]  Jonathan Loo,et al.  Texture Representation Through Overlapped Multi-Oriented Tri-Scale Local Binary Pattern , 2019, IEEE Access.

[14]  Jian-Huang Lai,et al.  Person Re-Identification by Camera Correlation Aware Feature Augmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Meng Wang,et al.  Cross-Entropy Adversarial View Adaptation for Person Re-Identification , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Changxin Gao,et al.  Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification † , 2019, Sensors.

[17]  Yasar Amin,et al.  Image Local Features Description Through Polynomial Approximation , 2019, IEEE Access.