Title :
Segment Model Based Vehicle Motion Analysis
Author :
Zhu, Pengfei ; Hu, Weiming ; Li, Xi ; Li, Li
Author_Institution :
Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China
Abstract :
Motion analysis is a very attractive research direction in computer vision field. In this paper, we propose a framework for analyzing real vehicle motion in visual traffic surveillance by using Segment Model (SM), which is a kind of probabilistic model. SM can grasp the underlying information of observation sequence by using segment distribution. It has been proved to be more precise than that of HMM. In the experiments, we compare our approach with the template matching method based on the Hausdorff distance and the state space method based on the Hidden Markov Model (HMM). The experimental results show the effectiveness of our approach.
Keywords :
hidden Markov models; image matching; image segmentation; motion compensation; probability; surveillance; traffic engineering computing; vehicles; Hausdorff distance; computer vision; hidden Markov model; probabilistic model; segment model; state space method; template matching; vehicle motion analysis; visual traffic surveillance; Computer vision; Decoding; Feature extraction; Frequency; Hidden Markov models; Intelligent vehicles; Motion analysis; Samarium; Smoothing methods; Surveillance; HMM; Hausdorff Distance; Motion Analysis; Segment Model;
Conference_Titel :
Advanced Video and Signal Based Surveillance, 2009. AVSS '09. Sixth IEEE International Conference on
Conference_Location :
Genova
Print_ISBN :
978-1-4244-4755-8
Electronic_ISBN :
978-0-7695-3718-4
DOI :
10.1109/AVSS.2009.9
