Author(s)

Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

.

Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.

Activity Pattern, Automatic Traffic Accident Recognition, Flow Gradient, Logistic Model

Sadek, S. , Al-Hamadi, A. , Michaelis, B. and Sayed, U. (2010) A Statistical Framework for Real-Time Traffic Accident Recognition. Journal of Signal and Information Processing, 1, 77-81. doi: 10.4236/jsip.2010.11008.