A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
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
Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
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
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Logic-based trajectory evaluation in videos
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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This article presents a visual object tracking method and applies an event-based performance evaluation metric for assessment. The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments. The 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, a performance evaluation method is presented and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These events are extracted automatically from the different trackers an their varying types of low level tracking results. Then, a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of multiple public datasets.