KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Viewpoint Invariant Person Re-Identification for Global Multi-Object Tracking with Non-Overlapping Cameras

  • Gwak, Jeonghwan (School of Electrical Engineering and Computer Science (EECS) Gwangju Institute of Science and Technology (GIST)) ;
  • Park, Geunpyo (School of Electrical Engineering and Computer Science (EECS) Gwangju Institute of Science and Technology (GIST)) ;
  • Jeon, Moongu (School of Electrical Engineering and Computer Science (EECS) Gwangju Institute of Science and Technology (GIST))
  • Received : 2016.05.18
  • Accepted : 2017.02.19
  • Published : 2017.04.30
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Abstract

Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.

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References

  1. S Bak, E Corvee, F Bremond, M. Thonnat, "Multiple-shot human re-identification by mean riemannian covariance grid," in Proc. of IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 179-184, 2011.
  2. S Bak, S Zaidenberg, B Boulay, F Bremond, "Improving person re-identification by viewpoint cues," in Proc. of IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 175-180, 2014.
  3. Z Wu, Y Li, RJ Radke, "Viewpoint invariant human re-identification in camera networks using pose priors and subject- discriminative features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, pp. 1095-1108, 2015. https://doi.org/10.1109/TPAMI.2014.2360373
  4. R Zhao, W Ouyang, X Wang, "Unsupervised salience learning for person re-identification," in Proc. of IEEE Conference on.Computer Vision and Pattern Recognition (CVPR), pp. 3586-3593, 2013.
  5. L Bazzani, M Cristani, V Murino, "Symmetry-driven accumulation of local features for human characterization and re-identification," Computer Vision and Image Understanding, vol. 117, pp. 130-144, 2013. https://doi.org/10.1016/j.cviu.2012.10.008
  6. DS Cheng, M Cristani, M Stoppa, L Bazzani, V Murino, "Custom Pictorial Structures for Re-identification," in Proc. of British Machine Vision Conference (BMVC), 2011.
  7. B Ma, Y Su, FB Jurie, "A novel image representation for person re-identification and face verification," in Proc. of British Machine Vision Conference (BMVC), 2012.
  8. S Bak, E Corvee, F Bremond, M Thonnat, "Person re-identification using haar-based and dcd-based signature," in Proc. of IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-8, 2010.
  9. M Dikmen, E Akbas, TS Huang, N Ahuja, "Pedestrian recognition with a learned metric," in Proc. of Asian Conference on Computer Vision (ACCV), pp. 501-512, 2011.
  10. WS Zheng, S Gong, T Xiang, "Reidentification by relative distance comparison," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 653-668, 2013. https://doi.org/10.1109/TPAMI.2012.138
  11. B Prosser, WS Zheng, S Gong, T Xiang, Q Mary, "Person Re-Identification by Support Vector Ranking," in Proc. of British Machine Vision Conference (BMVC), 2010.
  12. O Javed, K Shafique, Z Rasheed, M Shah, "Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views," Computer Vision and Image Understanding, vol. 109, pp. 146-162, 2008. https://doi.org/10.1016/j.cviu.2007.01.003
  13. D Makris, T Ellis, J Black, "Bridging the gaps between cameras," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. II-205, 2004.
  14. CC Loy, T Xiang, S Gong, "Multi-camera activity correlation analysis," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1988-1995, 2009.
  15. D Makris, T Ellis, "Automatic Learning of an Activity- Based Semantic Scene Model," in Proc. of IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2003.
  16. D Makris, T Ellis, "Path detection in video surveillance," Image and Vision Computing, vol. 20, pp. 895-903, 2002. https://doi.org/10.1016/S0262-8856(02)00098-7
  17. PE Forssen, "Maximally stable colour regions for recognition and matching," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2007.
  18. P Luo, X Wang, X Tang, "Pedestrian parsing via deep decompositional network," in Proc. of IEEE International Conference on Computer Vision (ICCV), pp. 2648-2655, 2013.
  19. D Baltieri, R Vezzani, R Cucchiara, "People orientation recognition by mixtures of wrapped distributions on random trees," in Proc. of European Conference on Computer Vision (ECCV), pp. 270-283, 2012.
  20. D Gray, S Brennan, H Tao, "Evaluating appearance models for recognition, reacquisition, and tracking," in Proc. of IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), 2007.
  21. Multi-Camera Object Tracking Challenge. http://mct.idealtest.org/datasets.html (August 2014).
  22. T Gandhi, MM Trivedi, "Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification," in Proc. of IEEE International Conference on Video and Signal Based Surveillance, pp. 78-82, 2006.
  23. D Baltieri, R Vezzani, R Cucchiara, "Mapping Appearance Descriptors on 3D Body Models for People Re-identification," International Journal of Computer Vision, vol. 111, no 3, pp. 345-364, 2015. https://doi.org/10.1007/s11263-014-0747-z
  24. R Vezzani, D Baltieri, R Cucchiara, "People reidentification in surveillance and forensics: a survey," ACM Computing Surveys, vol. 46, no. 2, pp. 29:1-29:37, 2013.
  25. G Doretto , T Sebastian , P Tu, J Rittscher, "Appearance-based person reidentification in camera networks: problem overview and current approaches," Journal of Ambient Intelligence and Humanized Computing, vol. 2, no. 2, pp 127-151, June 2011. https://doi.org/10.1007/s12652-010-0034-y
  26. S Liao, Y Hu, X Zhu, and SZ Li, "Person re-identification by local maximal occurrence representation and metric learning," in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, June 7-12, Boston, Massachusetts, USA, 2015.
  27. L Bazzani, M Cristani, A Perina, M Farenzena, and V Murino, "Multiple-shot person re-identification by HPE signature," in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, 2010.
  28. S Bak, R Kumar, and F Bremond, "Brownian descriptor: a rich meta-feature for appearance matching," in Proc. of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 363-370, 2014.
  29. T Wang, S Gong, X Zhu, and S Wang, "Person re-identification by video ranking," in Proc. of European Conference on Computer Vision (ECCV), 2014.
  30. M Zeng, Z Wu, C Tian, L Zhang, and L Hu, "Efficient person re-identification by hybrid spatiogram and covariance descriptor," in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition Workshops, 2015.
  31. M Hirzer, C Beleznai, PM Roth, and H Bischof, "Person re-identification by descriptive and discriminative classification," Image Analysis, 2011.