A traffic accident surveillance camera system through sound analysis based on frequency information
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Accident analysis of a surveillance camera system through a frequency-based processing
WSEAS Transactions on Circuits and Systems
Combining shadow detection and simulation for estimation of vehicle size and position
Pattern Recognition Letters
Vehicle tracking from disparate views
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
3-D model matching based on distributed estimation algorithm
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
Patch-based experiments with object classification in video surveillance
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
Vs-star: A visual interpretation system for visual surveillance
Pattern Recognition Letters
Vehicle class recognition using multiple video cameras
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Vehicle headlights detection using markov random fields
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Tracking vehicles as groups in airborne videos
Neurocomputing
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This paper aims at tracking vehicles from monocular intensity image sequences and presents an efficient and robust approach to three-dimensional (3-D) model-based vehicle tracking. Under the weak perspective assumption and the ground-plane constraint, the movements of model projection in the two-dimensional image plane can be decomposed into two motions: translation and rotation. They are the results of the corresponding movements of 3-D translation on the ground plane (GP) and rotation around the normal of the GP, which can be determined separately. A new metric based on point-to-line segment distance is proposed to evaluate the similarity between an image region and an instantiation of a 3-D vehicle model under a given pose. Based on this, we provide an efficient pose refinement method to refine the vehicle's pose parameters. An improved EKF is also proposed to track and to predict vehicle motion with a precise kinematics model. Experimental results with both indoor and outdoor data show that the algorithm obtains desirable performance even under severe occlusion and clutter.