ANOMALY DETECTION FOR QUATERNION-VALUED TRAFFIC SIGNALS

In this paper, a novel anomaly detection method is presented by using quaternion numbers to model traffic signals. A signal processing approach is proposed to deal with traffic surveillance. Traffic structures are depicted using directed graph models. The relationship among different traffic direction signals are represented through using quaternion numbers instead of individual representation of one particular direction. Multi-granularity local density-based method is adopted to perform anomaly detection for separate entry direction distribution (EDD) signals. Complex traffic signals are subsequently examined by exploring the relationship expressed with quaternion numbers. In such way, the anomaly detection complexity is reduced. Experimental results show that the proposed algorithm can achieve high detection rate. The overall average DSR of both AM and PM sessions is about 97.83%, which is better than the previous algorithm (96.67%) in the literature.