State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Recovery of upper body poses in static images based on joints detection
Pattern Recognition Letters
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
Quasi Monte Carlo partitioned filtering for visual human motion capture
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Wireless smart camera network for real-time human 3D pose reconstruction
Computer Vision and Image Understanding
Self-occlusion handling for human body motion tracking from 3D ToF image sequence
Proceedings of the 1st international workshop on 3D video processing
Real time multiple people tracking and pose estimation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Integrating multiple uncalibrated views for human 3D pose estimation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Finding human poses in videos using concurrent matching and segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Multiple people tracking and pose estimation with occlusion estimation
Computer Vision and Image Understanding
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Tracking in object action space
Computer Vision and Image Understanding
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Automatic initialization and tracking of human pose is an important task in visual surveillance. We present a part-based approach that incorporates a variety of constraints in a unified framework. These constraints include the kinematic constraints between parts that are physically connected to each other, the occlusion of one part by another and the high correlation between the appearance of certain parts, such as the arms. The location probability distribution of each part is determined by evaluating appropriate likelihood measures. The graphical (non-tree) structure representing the interdependencies between parts is utilized to “connect” such part distributions via nonparametric belief propagation. Methods are also developed to perform this optimization efficiently in the large space of pose configurations