A kernel particle filter multi-object tracking using gabor-based region covariance matrices

Abstract

This paper presents an approach to label and track multiple objects through both temporally and spatially significant occlusions. To this end, tracking is performed at both the region level and the object level. At the region level, a kernel based particle filter method is used to search for optimal region tracks which limits the scope of object trajectories. At the object level, each object is located based on adaptive appearance models, spatial distributions and inter-occlusion relationships. Region covariance matrices are used to model objects appearance. We analyzed the advantages of using Gabor functions as features and embedded them in the RCMs to get a more accurate descriptor. The proposed architecture is capable of tracking multiple objects even in the presence of periods of full occlusions. Results from experiments with real video data show the effectiveness of the approach hereby proposed.