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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoqin Zhang Zhiyong Liu Hong Qiao |
| Copyright Year | 2007 |
| Description | Author affiliation: Inst. of Autom., Chinese Acad. of Sci., Beijing (Xiaoqin Zhang; Zhiyong Liu; Hong Qiao) |
| Abstract | This paper presents an efficient and robust tracking algorithm based on multiple cues fusion in the Bayesian framework. This method characterizes the object to be tracked using a MOG (mixture of Gaussians) based appearance model and a chamfer-matching based shape model. A selective updating technique for the models is employed to accommodate for appearance and illumination changes. Meantime, the mean shift algorithm is embedded as the prior information into the Bayesian framework to give a heuristic prediction in the hypotheses generation process, which also alleviates the great computational load suffered by the conventional Bayesian tracker. Experimental results demonstrate that, compared with some existing works, the proposed algorithm has a better adaptability to changes of the object as well as the environments. |
| Sponsorship | IEEE Robotics and Automation Soc |
| Starting Page | 4614 |
| Ending Page | 4619 |
| File Size | 347541 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424406013 |
| ISSN | 10504729 |
| DOI | 10.1109/ROBOT.2007.364190 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-10 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Bayesian methods Target tracking Robotics and automation Gaussian processes Lighting Particle filters Shape measurement Mobile robots Embedded computing template update appearance model chamfer distance Bayesian tracker |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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