Multitarget tracking with a corner-based particle filter

This paper presents a multitarget tracking algorithm based on a particle filter framework that exploits a sparse distributed shape model to handle partial occlusions. The state vector is composed by a set of points of interest (i.e. corners) and it enables to jointly describe position and shape of the target. An efficient importance sampling strategy is developed to limit the number of used particles and it is based on multiple Kanade-Lucas-Tomasi (KLT) feature trackers used to estimate local motion. The importance sampling strategy adaptively handles KLT failures and partial occlusions. Particles weights are computed exploiting a shape matching technique combined with object local appearance encoded in color histograms of patches centered on the points of interest constituting the state. The proposed approach does not require background subtraction techniques and overcomes several common difficulties in the tracking domain as partial occlusions, object deformations, scale changes, abrupt motion and non-static background. Extensive experimental results are provided on challenging sequences to demonstrate the robustness of the algorithm.