Self-organizing maps
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Learning Motion Patterns in Surveillance Video using HMM Clustering
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Clustering of time series data-a survey
Pattern Recognition
Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers
Multimedia Tools and Applications
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Computer vision algorithms for intersection monitoring
IEEE Transactions on Intelligent Transportation Systems
A Traffic Accident Recording and Reporting Model at Intersections
IEEE Transactions on Intelligent Transportation Systems
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a novel traffic event classification approach using event severities at intersections. The proposed system basically learns normal and common traffic flow by clustering vehicle trajectories. Common vehicle routes are generated by implementing trajectory clustering with Continuous Hidden Markov Model. Vehicle abnormality is detected by observing maximum likelihoods of partial vehicle locations and velocities on underlying common route models. The second part of the work is based on extracting the severities of abnormality by deviation measurement using Coefficient of Variances method. By using abnormal event samples, two severity classes are built in order to recognize event severities by Support Vector Machines and k-Nearest Neighborhood algorithms. Experimental results show that the proposed model has high precision with satisfactory incident detection and event severity classification performance.