Road Traffic Anomaly Detection Based on Fuzzy Theory
Yanshan Li, Tianyu Guo, Rongjie Xia, Weixin Xie · IEEE Access · 2018
The road traffic scenes are usually complex and the traffic video is vulnerable to external factors such as light, weather, and obstructions. It is difficult to extract the traffic parameters and detect the traffic anomaly exactly with the existing image processing and analysis technologies under such uncertainty factors. Considering the advantages of the fuzzy theory in dealing with uncertain information, we use the fuzzy theory to handle the complex issues in traffic video surveillance and put forward a traffic anomaly detection algorithm. First, the fuzzy traffic flow is designed based on the virtual detection lines and the fuzzy theory. Second, the fuzzy traffic density is designed based on the pixel statistics and the fuzzy theory. Third, the target’s fuzzy motion state is designed based on the vehicle trajectory and the fuzzy theory. Besides, the relevant membership functions for these traffic parameters are designed to perform state evaluation. Finally, the traffic anomaly detection algorithm is designed based on above-mentioned fuzzy traffic parameters and fuzzy control rules. The experimental results show that the algorithm proposed performs well in road anomaly detection.