CASIA OpenIR  > 09年以前成果
Traffic accident prediction using 3-D model-based vehicle tracking
Hu, WM; Xiao, XJ; Xie, D; Tan, TN; Maybank, S
AbstractIntelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.
KeywordActivity Patterns Prediction Of Traffic Accidents Three-dimensional (3-d) Model-based Vehicle Tracking
WOS Research AreaEngineering ; Telecommunications ; Transportation
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS IDWOS:000221517200012
Citation statistics
Cited Times:99[WOS]   [WOS Record]     [Related Records in WOS]
Document Type专利
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Univ London Birkbeck Coll, Sch Comp Sci & Informat Syst, London WC1E 7HX, England
Recommended Citation
GB/T 7714
Hu, WM,Xiao, XJ,Xie, D,et al. Traffic accident prediction using 3-D model-based vehicle tracking[P]. 2004-05-01.
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