Salient Parts based Multi-people Tracking

Zhi Zhou, Yue Wang, Eam Khwang Teoh

2015

Abstract

The saliency of an object or area is the quality to stand out from its neighborhood, it is an important component when we observe objects in the real world. The detection of saliency has been studied for years and has already been applied in many areas. In this paper, salient parts based framework is proposed for multi-people tracking. The framework follows tracking-by-detection approach and performs multi-people tracking from frame to frame. Salient parts are detected inside the human body area by finding high contrasts to their local neighborhood. Short-term tracking of salient parts are applied to help locating targets when the association with detections fails. And supporting models are on-line learnt to indicate the locations of targets based on the tracking results of salient parts. Experiments are carried out on PETS09 and Town Center datasets to validate the proposed method. The experimental result shows the promising performance of the proposed method and comparison with state-of-the-art works is provided.

References

  1. Andriluka, M., Roth, S., and Schiele, B. (2008). People-tracking-by-detection and people-detectionby-tracking. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  2. Andriyenko, A. and Schindler, K. (2011). Multi-target tracking by continuous energy minimization. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1265-1272. IEEE.
  3. Benfold, B. and Reid, I. (2011). Stable multi-target tracking in real-time surveillance video. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 3457-3464. IEEE.
  4. Berclaz, J., Fleuret, F., Turetken, E., and Fua, P. (2011). Multiple object tracking using k-shortest paths optimization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(9):1806-1819.
  5. Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. (2011). Online multiperson tracking-by-detection from a single, uncalibrated camera. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(9):1820-1833.
  6. Checka, N., Wilson, K., Rangarajan, V., and Darrell, T. (2003). A probabilistic framework for multi-modal multi-person tracking. In Computer Vision and Pattern Recognition Workshop, 2003. CVPRW'03. Conference on, volume 9, pages 100-100. IEEE.
  7. Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., and Hu, S.-M. (2011). Global contrast based salient region detection. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 409- 416. IEEE.
  8. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893. IEEE.
  9. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
  10. Ferryman, J. and Shahrokni, A. (2009). Pets2009: Dataset and challenge. In Winter-PETS.
  11. Fleuret, F., Berclaz, J., Lengagne, R., and Fua, P. (2008). Multicamera people tracking with a probabilistic occupancy map. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2):267-282.
  12. Ge, W. and Collins, R. T. (2008). Multi-target data association by tracklets with unsupervised parameter estimation. In BMVC, volume 2, page 5.
  13. Grabner, H., Matas, J., Van Gool, L., and Cattin, P. (2010). Tracking the invisible: Learning where the object might be. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 1285- 1292. IEEE.
  14. Iodice, S. and Petrosino, A. (2013). Person re-identification based on enriched symmetry salient features and graph matching. In Pattern Recognition, pages 155- 164. Springer.
  15. Itti, L., Koch, C., and Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259.
  16. Izadinia, H., Saleemi, I., Li, W., and Shah, M. (2012). 2t: Multiple people multiple parts tracker. In Computer Vision-ECCV 2012, pages 100-114. Springer.
  17. Jiang, H., Fels, S., and Little, J. J. (2007). A linear programming approach for multiple object tracking. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pages 1-8. IEEE.
  18. Kalal, Z., Mikolajczyk, K., and Matas, J. (2010). Forwardbackward error: Automatic detection of tracking failures. In Pattern Recognition (ICPR), 2010 20th International Conference on, pages 2756-2759. IEEE.
  19. Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., and Zhang, J. (2009). Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2):319-336.
  20. Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97.
  21. Kuo, C.-H., Huang, C., and Nevatia, R. (2010). Multi-target tracking by on-line learned discriminative appearance models. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 685-692. IEEE.
  22. Leal-Taixé, L., Pons-Moll, G., and Rosenhahn, B. (2011). Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 120-127. IEEE.
  23. Li, Y., Hou, X., Koch, C., Rehg, J., and Yuille, A. (2014). The secrets of salient object segmentation. CVPR.
  24. Milan, A., Roth, S., and Schindler, K. (2014). Continuous energy minimization for multi-target tracking.
  25. Milan, A., Schindler, K., and Roth, S. (2013). Detectionand trajectory-level exclusion in multiple object tracking. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 3682- 3689. IEEE.
  26. Oh, S., Russell, S., and Sastry, S. (2004). Markov chain monte carlo data association for general multipletarget tracking problems. In Decision and Control, 2004. CDC. 43rd IEEE Conference on, volume 1, pages 735-742. IEEE.
  27. Ouyang, W. and Wang, X. (2013). Single-pedestrian detection aided by multi-pedestrian detection. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 3198-3205. IEEE.
  28. Perazzi, F., Krahenbuhl, P., Pritch, Y., and Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 733-740. IEEE.
  29. Pirsiavash, H., Ramanan, D., and Fowlkes, C. C. (2011). Globally-optimal greedy algorithms for tracking a variable number of objects. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1201-1208. IEEE.
  30. Segal, A. V. and Reid, I. (2013). Latent data association: Bayesian model selection for multi-target tracking. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 2904-2911. IEEE.
  31. Shitrit, B. H., Berclaz, J., Fleuret, F., and Fua, P. (2013). Multi-commodity network flow for tracking multiple people.
  32. Storms, P. P. and Spieksma, F. C. (2003). An lp-based algorithm for the data association problem in multitarget tracking. Computers & Operations Research, 30(7):1067-1085.
  33. Tang, S., Andriluka, M., and Schiele, B. (2012). Detection and tracking of occluded people. International Journal of Computer Vision, pages 1-12.
  34. Tian, Y., Li, J., Yu, S., and Huang, T. (2014). Learning complementary saliency priors for foreground object segmentation in complex scenes. International Journal of Computer Vision, pages 1-18.
  35. Wu, B. and Nevatia, R. (2006). Tracking of multiple, partially occluded humans based on static body part detection. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 1, pages 951-958. IEEE.
  36. Wu, Z., Thangali, A., Sclaroff, S., and Betke, M. (2012). Coupling detection and data association for multiple object tracking. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 1948-1955. IEEE.
  37. Yamaguchi, K., Berg, A. C., Ortiz, L. E., and Berg, T. L. (2011). Who are you with and where are you going? In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1345-1352. IEEE.
  38. Yang, B. and Nevatia, R. (2012a). An online learned crf model for multi-target tracking. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2034-2041. IEEE.
  39. Yang, B. and Nevatia, R. (2012b). Online learned discriminative part-based appearance models for multihuman tracking. In Computer Vision-ECCV 2012, pages 484-498. Springer.
  40. Yeh, H.-H., Liu, K.-H., and Chen, C.-S. (2014). Salient object detection via local saliency estimation and global homogeneity refinement. Pattern Recognition, 47(4):1740-1750.
  41. Zhang, J., Presti, L. L., and Sclaroff, S. (2012). Online multi-person tracking by tracker hierarchy. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on, pages 379-385. IEEE.
  42. Zhao, R., Ouyang, W., and Wang, X. (2013). Unsupervised salience learning for person re-identification. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 3586-3593. IEEE.
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Paper Citation


in Harvard Style

Zhou Z., Wang Y. and Teoh E. (2015). Salient Parts based Multi-people Tracking . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 231-240. DOI: 10.5220/0005262202310240


in Bibtex Style

@conference{visapp15,
author={Zhi Zhou and Yue Wang and Eam Khwang Teoh},
title={Salient Parts based Multi-people Tracking},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={231-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005262202310240},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Salient Parts based Multi-people Tracking
SN - 978-989-758-091-8
AU - Zhou Z.
AU - Wang Y.
AU - Teoh E.
PY - 2015
SP - 231
EP - 240
DO - 10.5220/0005262202310240