Multiple Likelihood Models based Particle Filter for Long-term Full Occlusion

Object tracking is one of the most important applications in the field of computer vision. One of the common problems in object tracking is object occlusions. Especially in the presence of long-term full occlusion, or called long-lived full occlusion, during which the target remains invisible for tens of frames, the tracking is more difficult. This paper proposes an occlusion handling scheme based on particle filter. Compared with the conventional particle filter which usually utilizes color as tracking cue, multiple likelihood models: HSV color and gradient orientation likelihoods, are employed in the observation model during occlusion. The incorporation of these two features makes the target distinguishable even if it is occluded by a similar colored object in the background. Also, multiple state noises are introduced to ensure the redetection of the target at the end of full occlusion as well as keeping tracking accuracy under occlusion. Experimental results under different occlusion conditions show that the proposed particle filter achieves robust and accurate performance compared with the particle filter with appearance adaptive models and the color particle filter, even in the condition of long-lived full occlusion.