W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
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
Robust recognition and segmentation of human actions using HMMs with missing observations
EURASIP Journal on Applied Signal Processing
Simultaneous tracking of multiple body parts of interacting persons
Computer Vision and Image Understanding
A matching-based approach for human motion analysis
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Real-time video content analysis tool for consumer media storage system
IEEE Transactions on Consumer Electronics
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This paper presents a novel and fast scheme to detect different body parts in human motion. Using monocular video sequences, trajectory estimation and body modeling of moving humans are combined in a co-operating processing architecture. More specifically, for every individual person, features of body ratio, silhouette and appearance are integrated into a hybrid model to detect body parts. The conventional assumption of upright body posture is not required. We also present a new algorithm for accurately finding the center point of the human body. The body configuration is finally described by a skeleton model. The feasibility and accuracy of the proposed scheme are analyzed by evaluating its performance for various sequences with different subjects and motion types (walking, pointing, kicking, leaping and falling). Our detection system achieves nearly real-time performance (around 10 frames/second).