Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Framework for Illumination Invariant Vehicular Traffic Density Estimation
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Determination of Gender and Age Based on Pattern of Human Motion Using AdaBoost Algorithms
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Robust object segmentation using probability-based background extraction algorithm
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
Learning to recognize video-based spatiotemporal events
IEEE Transactions on Intelligent Transportation Systems
Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
Controller for urban intersections based on wireless communications and fuzzy logic
IEEE Transactions on Intelligent Transportation Systems
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Vehicle counting without background modeling
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers
Multimedia Tools and Applications
Expert Systems with Applications: An International Journal
Real-Time and robust background updating for video surveillance and monitoring
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Advanced formation and delivery of traffic information in intelligent transportation systems
Expert Systems with Applications: An International Journal
The invariance properties of chromatic characteristics
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Scene Aware Detection and Block Assignment Tracking in crowded scenes
Image and Vision Computing
Public Space Behavior Modeling With Video and Sensor Analytics
Bell Labs Technical Journal
Traffic vehicle behavior prediction using hidden markov models
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
A low-bandwidth camera sensor platform with applications in smart camera networks
ACM Transactions on Sensor Networks (TOSN)
Euler Principal Component Analysis
International Journal of Computer Vision
INT3-Horus framework for multispectrum activity interpretation in intelligent environments
Expert Systems with Applications: An International Journal
Traffic event classification at intersections based on the severity of abnormality
Machine Vision and Applications
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We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness