Paper Info
Title
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.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00027
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Field
DocType
ISSN
Computer vision,Anomaly detection,Pattern learning,Computer science,Road traffic,Robustness (computer science),Static mode,Vehicle dynamics,Artificial intelligence,Dual-mode vehicle,Trajectory
Conference
2160-7508
Citations 
PageRank 
References 
2
0.34
0
Authors
9
Name
Order
Citations
PageRank
Xing Xu119927.30
Xi Ouyang2274.86
Yu Cheng361555.76
Shining Yu420.34
Lin Xiong5484.07
Choon-Ching Ng620.34
Sugiri Pranata7365.78
Sheng Mei Shen813113.13
Junliang Xing9119363.31