Abstract
We propose a novel system for associating multi-target tracks across multiple non-overlapping cameras by an on-line learned discriminative appearance affinity model. Collecting reliable training samples is a major challenge in on-line learning since supervised correspondence is not available at runtime. To alleviate the inevitable ambiguities in these samples, Multiple Instance Learning (MIL) is applied to learn an appearance affinity model which effectively combines three complementary image descriptors and their corresponding similarity measurements. Based on the spatial-temporal information and the proposed appearance affinity model, we present an improved inter-camera track association framework to solve the “target handover” problem across cameras. Our evaluations indicate that our method have higher discrimination between different targets than previous methods.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Porikli, F.: Inter-camera color calibration by correlation model function. In: ICIP (2003)
Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: CVPR (2005)
Chen, K.W., Lai, C.C., Hung, Y.P., Chen, C.S.: An adaptive learning method for target tracking across multiple cameras. In: CVPR (2008)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T., Pharmaceutical, A.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)
Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS (2005)
Babenko, B., Yang, M.H., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: CVPR (2009)
Cai, Q., Aggarwal, J.: Tracking human motion in structured environments using a distributed-camera system. IEEE Tran. on PAMI 21, 1241–1247 (1999)
Collins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE 89, 1456–1477 (2001)
Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Tran. on PAMI 25, 1355–1360 (2003)
Huang, T., Russell, S.: Object identification in a bayesian context. In: IJCAI (1997)
Pasula, H., Russell, S., Ostl, M., Ritov, Y.: Tracking many objects with many sensors. In: IJCAI (1999)
Kettnaker, V., Zabih, R.: Bayesian multi-camera surveillance. In: CVPR (1999)
Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameraswith disjoint views. In: ICCV (2003)
Dick, A.R., Brooks, M.J.: A stochastic approach to tracking objects across multiple cameras. In: Australian Conference on Artificial Intelligence (2004)
Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: CVPR (2004)
Gilbert, A., Bowden, R.: Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 125–136. Springer, Heidelberg (2006)
Sturges, J., Whitfield, T.: Locating basic colour in the munsell space. Color Research and Application 20, 364–376 (1995)
Song, B., Roy-Chowdhury, A.: Robust tracking in a camera network: A multi-objective optimization framework. IEEE Journal of Selected Topics in Signal Processing 2, 582–596 (2008)
Song, B., Roy-Chowdhury, A.K.: Stochastic adaptive tracking in a camera network. In: ICCV (2007)
Huang, C., Wu, B., Nevatia, R.: Robust object tracking by hierarchical association of detection responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)
Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent in function space (1999)
Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by on-line learned discriminative appearance models. In: CVPR (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuo, CH., Huang, C., Nevatia, R. (2010). Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15549-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-15549-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15548-2
Online ISBN: 978-3-642-15549-9
eBook Packages: Computer ScienceComputer Science (R0)