Abstract
This paper presents a novel social interaction relation, attraction (interaction that would lead to occlusion for inter-object) for multi-object tracking to handle occlusion issue. We propose to build attraction by utilizing spatial-temporal information from 2D image plane, such as decomposed distance between objects. Then pairwise attraction force is obtained by the modeled attraction. Lastly, the attraction force is used to improve tracking when hierarchical data association performs. To meet requirements of practical application, we have our method evaluated on widely used PETS 2009 datasets. Experimental results show that our method achieves results on par with, or better than state-of-the-art methods.
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Notes
- 1.
This work is performed when the first author was with Institut Mines Télécom, Paris. The author would like to thank Prof. Isabelle Bloch, Dr. Ling Wang and Dr. Henrique Morimits for meaningful discussion and very helpful suggestions.
References
Andriyenko, A., Roth, S., Schindler, K.: An analytical formulation of global occlusion reasoning for multi-target tracking. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1839–1846. IEEE (2011)
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intel. 33(9), 1820–1833 (2011)
Chang, X., Zheng, W.S., Zhang, J.: Learning Person-Person interaction in collective activity recognition. IEEE Trans. Image Proces. 24(6), 1905–1918 (2015)
Choi, W., Chao, Y.W., Pantofaru, C., Savarese, S.: Understanding indoor scenes using 3d geometric phrases. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 33–40. IEEE (2013)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intel. 32(9), 1627–1645 (2010)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
Hua, Y., Alahari, K., Schmid, C.: Occlusion and motion reasoning for long-term tracking. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 172–187. Springer, Heidelberg (2014)
Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., Zhang, J.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. IEEE Trans. Pattern Anal. Mach. Intel. 31(2), 319–336 (2009)
Kuhn, H.W.: The hungarian method for the assignment problem. In: 50 Years of Integer Programming 1958–2008, pp. 29–47. Springer, Heidelberg (2010)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptive color-based particle filter. Image Vis. Comput. 21(1), 99–110 (2003)
Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–268. IEEE (2009)
Possegger, H., Mauthner, T., Roth, P.M., Bischof, H.: Occlusion geodesics for online multi-object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Ramanathan, V., Yao, B., Fei-Fei, L.: Social role discovery in human events. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2475–2482. IEEE (2013)
Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.: Learning people detectors for tracking in crowded scenes. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1049–1056. IEEE (2013)
Yang, B., Nevatia, R.: Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1918–1925. IEEE (2012)
Zhang, J., Presti, L., Sclaroff, S.: Online Multi-person Tracking by Tracker Hierarchy. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 379–385, September 2012
Zhang, T., Jia, K., Xu, C., Ma, Y., Ahuja, N.: Partial occlusion handling for visual tracking via robust part matching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1258–1265. IEEE (2014)
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Li, Y., Bloch, I., Shen, W. (2015). Handling Inter-object Occlusion for Multi-object Tracking Based on Attraction Force Constraint. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_58
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DOI: https://doi.org/10.1007/978-3-319-20801-5_58
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