Abstract
Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditions such as minimal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates object tracks, estimates corresponding 2D/3D temporal trajectories in the camera reference system as well as estimates the model parameters (pose, focal length, etc) within a coherent probabilistic formulation. Since our goal is to estimate stable and robust tracks that can be univocally associated to the object IDs, we propose to include in our formulation an interaction (attraction and repulsion) model that is able to model multiple 2D/3D trajectories in space-time and handle situations where objects occlude each other. We use a MCMC particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking and camera model estimation. Qualitative and quantitative experimental results obtained using our own dataset and the publicly available ETH dataset shows very promising tracking and camera estimation results.
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
Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)
Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. In: CVPR (2006)
Project-webpage (2010), http://www.eecs.umich.edu/vision/mttproject.html
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI (2002)
Avidan, S.: Ensemble tracking. PAMI (2007)
Yin, Z., Collins, R.: On-the-fly object modeling while tracking. In: CVPR (2007)
Matthews, I., Ishikawa, T., Baker, S.: The template update problem. PAMI 26, 810–815 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. In: PAMI (2009)
Okuma, K., Taleghani, A., Freitas, N.D., Freitas, O.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)
Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors (2007)
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)
Khan, Z., Balch, T., Dellaert, F.: Mcmc-based particle filtering for tracking a variable number of interacting targets (2005)
Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: ICCV (2009)
Scovanner, P., Tappen, M.: Learning pedestrian dynamics from the real world. In: ICCV (2009)
Tomasi, C., Kanade, T.: Detection and tracking of point features. In: Carnegie Mellon University Technical Report (1991)
Kuhn, H.W.: The hungarian method for the assignment problem. In: Naval Research Logistics Quarterly (1955)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. PAMI 29, 1052–1067 (2007)
Smith, P., Reid, I., Davison, A.: Real-time monocular slam with straight lines. In: BMVC (2006)
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
Choi, W., Savarese, S. (2010). Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15561-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-15561-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15560-4
Online ISBN: 978-3-642-15561-1
eBook Packages: Computer ScienceComputer Science (R0)