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
Arm Gesture Detection in a Classroom Environment
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Recognizing Hand-Raising Gestures using HMM
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
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
Recognition of Hand Raising Gestures for a Remote Learning Application
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Detection of hand-raising gestures based on body silhouette analysis
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Hi-index | 0.01 |
This paper introduces a method of hand-raising gestures detection in indoor environments, using shape and edge features. Past approaches have detected the gestures through recognizing the action for isolated or seated persons. Here, to deal with movements, non-rigidity and partially occlusions of human bodies, the gestures are detected by searching for raised hands and arms rather than recognizing the action. First, background subtraction is employed to obtain body silhouette. And then, according to the particular shape edge features of raised hands and arms, CR (candidate region) search, R-transform based shape and GLAC edge features extraction and classification, are applied to find raised hands. The classification is implemented by a hierarchical detector which consists of four SVM classifiers. Experiments show that this method can detect hand-raising gestures well, even for moving persons in crowd.