Multi-resolution Tracking in Space and Time
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera selection for tracking in distributed smart camera networks
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
This paper presents a novel tracking method with the multiresolution 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.