Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Extraction of Parametric Human Model for Posture Recognition Using Genetic Algorithm
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Recognizing Human Behavior Using Universal Eigenspace
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Detecting moving objects, ghosts, and shadows in video streams
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
Real-Time fall detection method based on hidden markov modelling
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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In this paper we present a people posture classification approach especially devoted to cope with occlusions. In particular, the approach aims at assessing temporal coherence of visual data over probabilistic models. A mixed predictive and probabilistic tracking is proposed: a probabilistic tracking maintains along time the actual appearance of detected people and evaluates the occlusion probability; an additional tracking with Kalman prediction improves the estimation of the people position inside the room. Probabilistic Projection Maps (PPMs) created with a learning phase are matched against the appearance mask of the track. Finally, an Hidden Markov Model formulation of the posture corrects the frame-by-frame classification uncertainties and makes the system reliable even in presence of occlusions. Results obtained over real indoor sequences are discussed.