Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Monocular tracking of the human arm in 3D
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Monocular Perception of Biological Motion Detection and Labeling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple Hypothesis Tracking for Automatic Optical Motion Capture
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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The problem of detecting and labeling a moving human body viewed monocularly in a cluttered scene is considered. The task is to decide whether or not one or more people are in the scene (detection), to count them, and to label their visible body parts (labeling). It is assumed that a motion-tracking front end is supplied: a number of moving features, some belonging to the body and some to the background are tracked for two frames and their position and velocity is supplied (Johansson display). It is not guaranteed that all the body parts are visible, nor that the only motion present is the one of the body. The algorithm is based on our previous work [12]; we learn a probabilistic model of the position and motion of body features, and calculate maximum-likelihood labels efficiently using dynamic programming on a triangulated approximation of the probabilistic model. We extend those results by allowing an arbitrary number of body parts to be undetected (e.g. because of occlusion) and by allowing an arbitrary number of noise features to be present. We train and test on walking and dancing sequences for a total of approximately 104 frames. The algorithm is demonstrated to be accurate and efficient.