Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection

Abstract

Anomaly detection on road traffic is an important task due to its great potential in urban traffic management and road safety. It is also a very challenging task since the abnormal event happens very rarely and exhibits different behaviors. In this work, we present a model to detect anomaly in road traffic by learning from the vehicle motion patterns in two distinctive yet correlated modes, i.e., the static mode and the dynamic mode, of the vehicles. The static mode analysis of the vehicles is learned from the background modeling followed by vehicle detection procedure to find the abnormal vehicles that keep still on the road. The dynamic mode analysis of the vehicles is learned from detected and tracked vehicle trajectories to find the abnormal trajectory which is aberrant from the dominant motion patterns. The results from the dual-mode analyses are finally fused together by driven a re-identification model to obtain the final anomaly. Experimental results on the Track 2 testing set of NVIDIA AI CITY CHALLENGE show the effectiveness of the proposed dual-mode learning model and its robustness in different real scenes. Our result ranks the first place on the final Leaderboard of the Track 2.