Abstract
A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and multiple particle filters. First, feature points are detected by a Harris corner detector and tracked by a Lucas-Kanade tracker. Clusters of moving targets are then initialized by grouping spatially co-located points with similar motion using the EM algorithm. Due to the non-Gaussian distribution of the points in a cluster and the multi-modality resulting from multiple targets, multiple particle filters are applied to track all the clusters simultaneously: one particle filter is started for one cluster. The proposed method is well suited for the typical video surveillance configuration where the cameras are still and targets of interest appear relatively small in the image. We demonstrate the effectiveness of our method on different PETS datasets.
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References
Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1988)
Lucas, D.B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Arnaud, E., Memin, E., Cernuschi-Frias, B.: A Robust Stochastic Filter for Point Tracking in Image Sequences. In: Asian Conference on Computer Vision, Korea (2004)
Shafique, K., Shah, M.: A Noniterative Greedy Algorithm for Multiframe Point Correspondence. IEEE Transactions on PAMI 27(1), 51–65 (2005)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society 39(1), 1–38 (1977)
Lookingbill, A., Lieb, D., Stavens, D., Thrun, S.: Learning Activity-based Ground Models from a Moving Helicopter Platform. In: ICRA, Spain (2005)
Ferryman, J.: PETS (2001), Datasets http://visualsurveillance.org/PETS2001
Pece, A.E.C.: Generative-Model-Based Tracking by Cluster Analysis of Image Differences. Robotics and Autonomous Systems 49(3), 181–194 (2002)
Du, W., Piater, J., Verly, J.: Tracking by Perceptually Grouping Feature Points into Clusters (submitted)
Doucet, A., Freitas, N., Godor, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2000)
Medioni, G., Tang, C.K.: Inference of Integrated Surface, Curve and Junction Descriptions from Sparse 3-D Data. IEEE Transactions on PAMI 20(11), 1206–1223 (1998)
Vermaak, J., Doucet, A., Perez, P.: Maintaining Multi-Modality through Mixture Tracking. In: International Conference on Computer Vision, Nice, France (2003)
Okuma, K., Taleghani, A., Freitas, N.D., Little, J.J., Lowe, D.G.: A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)
Piater, J., Crowley, J.: Multi-Modal Tracking of Interacting Targets Using Gaussian Ap¬proximations. In: Proceedings of the Second IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Hawaii, USA (2001)
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Du, W., Piater, J. (2005). Tracking by Cluster Analysis of Feature Points and Multiple Particle Filters. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_77
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DOI: https://doi.org/10.1007/11552499_77
Publisher Name: Springer, Berlin, Heidelberg
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