A multi-resolution particle filter tracking in a multi-camera environment
Yifan Zhou, Henri Nicolas, Jenny Benois‐Pineau · 2009
This paper presents a novel tracking method with the multi-resolution technique and a Kolmogrov-Smirnov test for model update to track a non-rigid target in an uncalibrated multi-camera environment. It is based on particle filter method using color appearance model. Compared to the related work, our method improves the tracking performance by proposing: i) a multi-resolution technique to rapidly locate the estimate of the target state and refine it gradually, ii) the Kolmogrov-Smirnov test to evaluate the reliability of the estimate so as to take the decision on further updating/ reinitialization of the estimate, as well as iii) an interaction of cameras approach to reinitialize the estimate by information detected in other cameras in case of tracking failures. After being tested in a multi-camera environment for one person tracking, our system is shown to give a better tracking result in comparison with mono-camera tracking, especially when occlusions occur.