Robust Visual Tracking for Multiple Targets

We address the problem of robust multi-target tracking within the application of hockey player tracking. Although there has been extensive work in multi-target tracking, there is no existing visual tracking system that can automatically and robustly track a variable number of targets and correctly maintain their identities with a monocular camera regardless of background clutter, camera motion and frequent mutual occlusion between targets. We build our system on the basis of the previous work by Okuma et al. [OTdF+04]. The particle filter technique is adopted and modified to fit into the multi-target tracking framework. A rectification technique is employed to map the locations of players from the video frame coordinate system to the standard hockey rink coordinates so that the system can compensate for camera motion and the dynamics of players on the rink can be improved by a second order auto-regression model. A global nearest neighbor data association algorithm is introduced to assign boosting detections to the existing tracks for the proposal distribution in particle filters. The mean-shift algorithm is embedded into the particle filter framework to stabilize the trajectories of the targets for robust tracking during mutual occlusion. The color model of the targets is also improved by the kernel introduced by mean-shift. Experimental results show that our system is able to correctly track all the targets in the scene even if they are partially or completely occluded for a period of time.