Tracking a group of highly correlated targets

Under the probabilistic framework, we consider the problem of tracking a group of highly correlated targets and propose to embed the correlation into the sampling procedure, where the correlation serves as both a prior information to improve the efficiency and a constraint to prevent trackers from confusion or drifting. Experiments under different settings demonstrate promising results in robustness with linear complexity.

[1]  Jean-Marc Odobez,et al.  Using particles to track varying numbers of interacting people , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Dan Schonfeld,et al.  Real-Time Distributed Multi-Object Tracking Using Multiple Interactive Trackers and a Magnetic-Inertia Potential Model , 2007, IEEE Transactions on Multimedia.

[4]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ying Wu,et al.  Decentralized multiple target tracking using netted collaborative autonomous trackers , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Yuan F. Zheng,et al.  Sequential Particle Generation for Visual Tracking , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Ramakant Nevatia,et al.  Tracking multiple humans in complex situations , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  James J. Little,et al.  Robust Visual Tracking for Multiple Targets , 2006, ECCV.

[9]  Mubarak Shah,et al.  Detecting group activities using rigidity of formation , 2005, MULTIMEDIA '05.