期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2012
卷号：9
DOI：10.5772/51613
语种：English
出版社：SAGE Publications
摘要：Object tracking is an important and fundamental task in computer vision and its high-level applications, e.g., intelligent surveillance, motion-based recognition, video indexing, traffic monitoring and vehicle navigation. However, the recent widespread use of wireless consumer cameras often produces low quality videos with frame-skipping and this makes object tracking difficult. Previous tracking methods, for example, generally depend heavily on object appearance or motion continuity and cannot be directly applied to frame-skipping videos. In this paper, we propose an improved particle filter for object tracking to overcome the frame-skipping difficulties. The novelty of our particle filter lies in using the detection result of erratic motion to ameliorate the transition model for a better trial distribution. Experimental results show that the proposed approach improves the tracking accuracy in comparison with the state-of-the-art methods, even when both the object and the consumer are in motion.
