A multi-resolution particle filter tracking with a dual consistency check for model update in a multi-camera environment

This paper presents a novel tracking method with a multi-resolution technique and a dual consistency check for model update to track a non-rigid target in an uncalibrated static multi-camera environment. It is based on particle filter methods using color appearance model. Compared to our previous work, the performance of tracking system is improved by proposing: i) a dual consistency check by Kolmo-grov-Smirnov test to evaluate the consistency of target estimate and ii) an interaction of cameras step by weighted least-squares method to compute the adaptive camera transformation matrix which is used to relocate the estimate in one camera by those in other cameras when tracking failure happens. After being tested in our multi-camera environment of one person tracking, a low failure rate in addition to a better tracking precision is achieved compared to mono-camera tracking.