Bayesian Pixel Classification for Human Tracking
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Real-Time Wide Area Multi-Camera Stereo Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Event-Based Tracking Evaluation Metric
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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This paper describes a monocular object tracker, able to detect and track multiple object classes in non-controlled environments. Our tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, semantically high level events are automatically extracted from the tracking data for performance evaluation. The reliability of the event detection is demonstrated by applying it to state-of-the-art methods and comparing the results to human annotated ground truth data for multiple public datasets.