NDLI: An approach to detecting abnormal vehicle events in complex factors over highway surveillance video

Content Provider	Springer Nature Link
Author	Sheng, Hao Xiong, Zhang Weng, Jingng Wei, Qi
Copyright Year	2008
Abstract	The detection of abnormal vehicle events is a research hotspot in the analysis of highway surveillance video. Because of the complex factors, which include different conditions of weather, illumination, noise and so on, vehicle’s feature extraction and abnormity detection become difficult. This paper proposes a Fast Constrained Delaunay Triangulation (FCDT) algorithm to replace complicated segmentation algorithms for multi-feature extraction. Based on the video frames segmented by FCDT, an improved algorithm is presented to estimate background self-adaptively. After the estimation, a multi-feature eigenvector is generated by Principal Component Analysis (PCA) in accordance with the static and motional features extracted through locating and tracking each vehicle. For abnormity detection, adaptive detection modeling of vehicle events (ADMVE) is presented, for which a semi-supervised Mixture of Gaussian Hidden Markov Model (MGHMM) is trained with the multi-feature eigenvectors from each video segment. The normal model is developed by supervised mode with manual labeling, and becomes more accurate via iterated adaptation. The abnormal models are trained through the adapted Bayesian learning with unsupervised mode. The paper also presents experiments using real video sequence to verify the proposed method.
Starting Page	199
Ending Page	208
Page Count	10
File Format	PDF
ISSN	10069321
Journal	Science in China Series E: Technological Sciences
Volume Number	51
Issue Number	2
e-ISSN	1862281X
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-03-14
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	background estimation abnormity detection adaptive detection modeling a semi-supervised MGHMM Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Materials Science

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