Rx for semantic video database retrieval
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Motion recovery for video content classification
ACM Transactions on Information Systems (TOIS) - Special issue on video information retrieval
Multi-texture modeling of 3D traffic scenes
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
OATS: Oxford Aerial Tracking System
Robotics and Autonomous Systems
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Real time detection and tracking of vehicles for speed measurement and license plate detection
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
A PCA-based technique to detect moving objects
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Proceedings of the 7th International Conference on Frontiers of Information Technology
Toward to smart world: self awareness/adaptive traffic signal control system
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Self awareness and adaptive traffic signal control system for smart world
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
A multiscale co-linearity statistic based approach to robust background modeling
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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In this work we address the problem of occlusion in tracking multiple 3D objects in a known environment and propose a new approach for tracking vehicles in road traffic scenes using an explicit occlusion reasoning step. We employ a contour tracker based on intensity and motion boundaries. The motion of the contour of the vehicles in the image is assumed to be well describable by an affine motion model with a translation and a change in scale. A vehicle contour is represented by closed cubic splines the position and motion of which is estimated along the image sequence. In order to employ linear Kalman Filters we decompose the estimation process into two filters: one for estimating the affine motion parameters and one for estimating the shape of the contours of the vehicles. Occlusion detection is performed by intersecting the depth ordered regions associated to the objects. The intersection part is then excluded in the motion and shape estimation. This procedure also improves the shape estimation in case of adjacent objects since occlusion detection is performed on slightly enlarged regions. In this way we obtain robust motion estimates and trajectories for vehicles even in the case of occlusions, as we show in some experiments with real world traffic scenes.