Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers

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.