ICTACT Journal on Image and Video Processing

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Video Based Person Re-Identification using Support Vector Machine and Long Short Term Memory


Affiliations
1 School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, India
     

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We address the problem of re-identifying a person in videos captured from cameras with disjoint field of view in different background. The varying pose, scale, and motion pattern of a person makes this task challenging. In this work we propose two frameworks wherein the first one exploits only the appearance information whereas the second incorporates motion information as well. The first framework utilizes Support Vector Machine (SVM) and inner product for measuring the similarity between query and gallery videos. In the next framework we present a neural network based system for re-identifying persons in videos. We employ an LSTM as a classifier and train it over output vectors from the memory cells corresponding to different persons obtained from another LSTM. Both the proposed methods outperform existing state-of-the-art methods.

Keywords

Video based Re-identification, Support Vector Machine (SVM), Classification Score, Convolution Neural Network (CNN), Long Short Term Memory (LSTM), Sequence Classifier.
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  • S. Tang, M. Andriluka, B. Andres and B. Schiele, “Multiple People Tracking by Lifted Multicut and Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3539-3548, 2017.

  • A. Hermans, L. Beyer and B. Leibe, “In Defense of the Triplet Loss for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2333-2339, 2017.

  • Y. Liu, J. Yan and W. Ouyang, “Quality Aware Network for Set to Set Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5790-5799, 2017.

  • T. Wang, S. Gong, X. Zhu and S. Wang, “Person Re-Identification by Discriminative Selection in Video Ranking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 12, pp. 2501-2514, 2016.

  • S. Xu, Y. Cheng, K. Gu, Y. Yang, S. Chang and P. Zhou, “Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision, pp. 4733-4742, 2017.

  • W. Zhang, S. Hu and K. Liu, “Learning Compact Appearance Representation for Video-based Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4660-4669, 2017.

  • C. Su, J. Li, S. Zhang, J. Xing, W. Gao and Q. Tian, “Pose Driven Deep Convolutional Model for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision, pp. 3960-3969, 2017.

  • L. Zheng, Y. Huang, H. Lu and Y. Yang, “Pose Invariant Embedding for Deep Person Re-Identification”, IEEE Transactions on Image Processing, Vol. 28, No. 9, pp. 4500-4509, 2019.

  • X. Tang, “Spindle Net: Person Re-Identification with Human Body Region Guided Feature Decomposition and Fusion”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1077-1085, 2017.

  • D. Li, X. Chen, Z. Zhang and K. Huang, “Learning Deep Context-Aware Features Over Body and Latent Parts for Person RE- Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 384-393, 2017.

  • L. Zhao, X. Li, Y. Zhuang and J. Wang, “Deeply-Learned Part- Aligned Representations for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision, pp. 3219–3228, 2017.

  • H. Fan, L. Zheng, C. Yan and Y. Yang, “Unsupervised Person Re- Identification: Clustering and Fine-Tuning”, ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 14, No. 4, pp. 83-93, 2018.

  • M. Ye, A.J. Ma, L. Zheng, J. Li and P. C. Yuen, “Dynamic Label Graph Matching for Unsupervised Video Re-Identification”, Proceedings of IEEE Conference on Computer Vision, pp. 5142-5150, 2017.

  • S. Liao, Y. Hu, X. Zhu and S.Z. Li, “Person Re-Identification by Local Maximal Occurrence Representation and Metric Learning”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197-2206, 2015.

  • D. Chen, Z. Yuan, G. Hua, N. Zheng and J. Wang, “Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1565-1573, 2015.

  • D. Chen, Z. Yuan, B. Chen and N. Zheng, “Similarity Learning with Spatial Constraints for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1268-1277, 2016.

  • L. Zhang, T. Xiang and S. Gong, “Learning a Discriminative Null Space for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1239-1248, 2016.

  • Y. Zhang, B. Li, H. Lu, A. Irie and X. Ruan, “Sample-Specific SVM Learning for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1278-1287, 2016.

  • L. Zheng, S. Wang, L. Tian, F. He, Z. Liu and Q. Tian, “Query-Adaptive Late Fusion for Image Search and Person Re- Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741-1750, 2015.

  • C. Su, F. Yang, S. Zhang, Q. Tian, L.S. Davis and W. Gao, “Multi Task Learning with Low Rank Attribute Embedding for Person Re- Identification”, Proceedings of IEEE Conference on Computer Vision, pp. 3739-3747, 2015.

  • N. Martinel, A. Das, C. Micheloni and A. K. Roy Chowdhury, “Temporal Model Adaptation for Person Re-Identification”, Proceedings of European Conference on Computer Vision, pp. 858-877, 2016.

  • L. Zheng, Y. Yang and A.G. Hauptmann, “Person Re- Identification: Past, Present and Future”, Proceedings of European Conference on Computer Vision, pp. 668-687, 2016.

  • D. Gray and H. Tao, “Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features”, Proceedings of European Conference on Computer Vision, pp. 262-275, 2008.

  • A. Mignon and F. Pcca, “A New Approach for Distance Learning from Sparse Pairwise Constraints”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1197-1206, 2012.

  • R. Zhao, W. Ouyang and X. Wang, “Unsupervised Salience Learning for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586-3593, 2013.

  • M. Koestinger, M. Hirzer, P. Wohlhart, P.M. Roth and H. Bischof, “Large Scale Metric Learning from Equivalence Constraints”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288-2295, 2012.

  • R. Zhao, W. Ouyang and X. Wang, “Learning Mid-Level Filters for Person Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 144-151, 2014.

  • A. Dehghan, and M. Shah, “Visual Tracking: An Experimental Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 7, pp. 1442-1468, 2014.

  • A. Karpathy, J. Johnson, and L. Fei Fei, “Visualizing and Understanding Recurrent Networks”, Proceedings of International Conference on Learning Representations, pp. 1-11, 2015.

  • M. Gou, S. Karanam, W. Liu, O. Camps and R.J. Radke, “DukeMMTMC4ReID: A Large-Scale Multi-Camera Person Re- Identification Dataset”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 10-19, 2017.

  • E. Ristani and C. Tomasi, “Features for Multi-Target Multi-Camera Tracking and Re-Identification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 6036-6046, 2018.

  • Y. Chen, X. Zhu and S. Gong, “Person Re-Identification by Deep Learning Multi-Scale Representations”, Proceedings of IEEE Conference on Computer Vision, pp. 2590-2600, 2017.

  • A.S. Hare and P.H. Torr, “Struck: Structured Output Tracking with Kernels”, Proceedings of IEEE Conference on Computer Vision, pp. 1-8, 2011.

  • D. Gordon, A. Farhadi and D. Fox, “Re3: Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects”, IEEE Robotics and Automation Letters, Vol. 3, No. 2, pp. 788-795, 2018.


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  • Video Based Person Re-Identification using Support Vector Machine and Long Short Term Memory

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Authors

Jyoti Nigam
School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, India

Abstract


We address the problem of re-identifying a person in videos captured from cameras with disjoint field of view in different background. The varying pose, scale, and motion pattern of a person makes this task challenging. In this work we propose two frameworks wherein the first one exploits only the appearance information whereas the second incorporates motion information as well. The first framework utilizes Support Vector Machine (SVM) and inner product for measuring the similarity between query and gallery videos. In the next framework we present a neural network based system for re-identifying persons in videos. We employ an LSTM as a classifier and train it over output vectors from the memory cells corresponding to different persons obtained from another LSTM. Both the proposed methods outperform existing state-of-the-art methods.

Keywords


Video based Re-identification, Support Vector Machine (SVM), Classification Score, Convolution Neural Network (CNN), Long Short Term Memory (LSTM), Sequence Classifier.

References