Tracking and data association
Sensor and Data Fusion Concepts and Applications
Sensor and Data Fusion Concepts and Applications
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Dynamic Template Update and Occlusion Detec
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
An Object-Oriented Approach to Simulating Human Gait Motion Based on Motion Tracking
International Journal of Applied Mathematics and Computer Science - Verified Methods: Applications in Medicine and Engineering
Performance evaluation metrics and statistics for positional tracker evaluation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching
Multimedia Tools and Applications
Tracking and labelling of interacting multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly. In this paper a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation, and robust tracking system. The proposed tracking strategy is based on scenes captured by static video cameras. Each camera is attached to a workstation that analyses its stream. All workstations are connected to a tracking server, which harmonises the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy, which has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy.