ECCV 2012: Computer Vision – ECCV 2012 pp 343-356 | Cite as
GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
Abstract
Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes.We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of Town Center [1], TUD-crossing [2], TUD-Stadtmitte [2], PETS2009 [3], and a new sequence called Parking Lot compared to the state of the art methods.
Keywords
Data Association Human Tracking Generalized Graphs GMCP Generalized Minimum Clique ProblemPreview
Unable to display preview. Download preview PDF.
References
- 1.Benfold, B., Reid, I.: Stable multi-target tracking in real time surveillance video. In: CVPR (2011)Google Scholar
- 2.Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: CVPR (2011)Google Scholar
- 3.Ferryman, J., Shahrokni, A.: Pets2009: Dataset and challenge. In: International Workshop on Performance Evaluation of Tracking and Surveillance (2009)Google Scholar
- 4.Shafique, K., Shah, M.: A noniterative greedy algorithm formultiframe point correspondence. In: PAMI (2005)Google Scholar
- 5.Shu, G., Dehghan, A., Oreifej, O., Hand, E., Shah, M.: Part-based multiple-person tracking with partial occlusion handling. In: CVPR (2012)Google Scholar
- 6.Leibe, B., Schindler, K., Gool, L.V.: Coupled detection and trajectory estimation for multi-object tracking. In: ICCV (2007)Google Scholar
- 7.Yamaguchi, K., Berg, A., Ortiz, L., Berg, T.: Who are you with and where are you going? In: CVPR (2011)Google Scholar
- 8.Leal-Taixe, L., et al.: Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In: ICCV 2011 Workshops (2011)Google Scholar
- 9.Pellegrini, S., Ess, A., Van Gool, L.: Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 10.Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
- 11.Brendel, W., Amer, M., Todorovic, S.: Multiobject tracking as maximumweight independent set. In: CVPR (2011)Google Scholar
- 12.Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. In: PAMI (2011)Google Scholar
- 13.Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global appearance constraints. In: ICCV (2011)Google Scholar
- 14.Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. In: PAMI (2010)Google Scholar
- 15.Feremans, C., Labbe, M., Laporte, G.: Generalized network design problems. In: EJOR (2003)Google Scholar
- 16.Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)Google Scholar
- 17.Andriluka, M., Roth, S., Schiele, B.: Monocular 3d pose estimation and tracking by detection. In: CVPR (2010)Google Scholar
- 18.Kasturi, R.: et al.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. In: PAMI (2009)Google Scholar
- 19.Henriques, J.F., Caseiro, R., Batista, J.: Globally optimal solution to multi-object tracking with merged measurements. In: ICCV (2011)Google Scholar
- 20.Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)Google Scholar