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
Probabilistic Methods for Finding People
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Detection and Modeling of Human-Body Parts from Monocular Video
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Multimedia Tools and Applications
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This paper presents a novel approach to implement estimation and recognition of human motion from uncalibrated monocular video sequences. As it is difficult to find a good motion description for humans, we propose a matching scheme based on a local descriptor and a global descriptor, to detect individual body parts and analyze the shape of the whole body as well. In a frame-by-frame process, both descriptors are combined to implement the matching of the motion pattern and the body orientation. Moreover, we have added a novel spatial-temporal cost factor in the matching scheme which aims at increasing the temporal consistency and reliability of the description. We tested the algorithms on the CMU MoBo database with promising results. The method achieves the motion-type recognition and body-orientation classification at the accuracy of 95% and 98%, respectively. The system can be utilized for an effective human-motion analysis from a monocular video.