Overlapping vehicle tracking via adaptive particle filter with multiple cues

Khong, Wei Leong and Kow, Wei Yeang and Chin, Yit Kwong and Ismail Saad and Teo, Kenneth Tze Kin (2011) Overlapping vehicle tracking via adaptive particle filter with multiple cues. In: 2011 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2011, 25-27 November 2011, Penang, Malaysia.

Full text not available from this repository.


Vehicle tracking is a vital approach to assist the on-road traffic surveillance system. Since the on-road vehicles is increasing, occlusion and overlapping of vehicles is often happen in the traffic surveillance scene. Therefore, segmentation and tracking of the occlusion or overlapped vehicle can be a challenging task in surveillance system via image processing. In this paper, a multiple cues overlapping vehicle tracking algorithm is proposed to continuously track the occluded vehicle effectively. The earlier vehicle tracking systems are normally based on colour feature which will leads to inaccurate results when the background colour is complex or too similar with the target vehicle. On the other hand, shape feature will increase the accuracy but consume more computation time in the resampling process during overlapping. The experimental results show that enhancement of the particle filter resampling process with multiple cues is capable to track the overlapped vehicle with higher accuracy and without compromising the processing time.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Keyword: Likelihood , Multiple cues , Particle filter , Vehicle tracking
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Department: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 12 Jul 2012 16:32
Last Modified: 30 Dec 2014 09:39
URI: https://eprints.ums.edu.my/id/eprint/4467

Actions (login required)

View Item View Item