The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Development of Incident Detection Model Using Neuro-Fuzzy Algorithm
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Computer vision algorithms for intersection monitoring
IEEE Transactions on Intelligent Transportation Systems
A Traffic Accident Recording and Reporting Model at Intersections
IEEE Transactions on Intelligent Transportation Systems
Traffic event classification at intersections based on the severity of abnormality
Machine Vision and Applications
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With the development of modern intelligent transportation systems (ITS), automatic traffic incident detection with quick response and high accuracy becomes one of the most important issues, especially for metropolitan streets that are full of signaled intersections. In this paper, we present our up-to-date research outcomes of the traffic incident detection system, which makes use of the image sequences gathered from a typical urban intersection. Basic image signal processing was used to extract image difference information for traffic image database construction. Feature extraction algorithms were then discussed and compared including PCA, FFT, and hybrid analysis of DCT-FFT. Finally, multi-classification of traffic signal logics (East---West, West---East, South---North, North---South) and accidents were realized by HMM (Hidden Markov Model) and SVM (Support Vector Machine) respectively. Experimental results showed that the hybrid DCT-FFT method gives the best features, and classification performance of SVM is superior to HMM with limited training samples, where the correction rate is 100% for SVM and 91% for HMM.