TY - GEN
T1 - Hierarchical probabilistic network-based system for traffic accident detection at intersections
AU - Hwang, Ju Won
AU - Lee, Young Seol
AU - Cho, Sung Bae
PY - 2010
Y1 - 2010
N2 - Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.
AB - Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.
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U2 - 10.1109/UIC-ATC.2010.27
DO - 10.1109/UIC-ATC.2010.27
M3 - Conference contribution
AN - SCOPUS:78651412810
SN - 9780769542720
T3 - Proceedings - Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with the UIC 2010 and ATC 2010 Conferences, UIC-ATC 2010
SP - 211
EP - 216
BT - Proceedings - Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with the UIC 2010 and ATC 2010 Conferences, UIC-ATC 2010
T2 - Symposia and Workshops Held in Conjunction with the 7th International Conference on Ubiquitous Intelligence and Computing, UIC 2010 and the 7th International Conference on Autonomic and Trusted Computing, ATC 2010
Y2 - 26 October 2010 through 29 October 2010
ER -
