Contextual Priming for Object Detection
International Journal of Computer 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
A survey on vision-based human action recognition
Image and Vision Computing
Visual object-action recognition: Inferring object affordances from human demonstration
Computer Vision and Image Understanding
Functional categorization of objects using real-time markerless motion capture
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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This paper proposes a novel approach to recognize actions and objects within the context of each other. Assuming that the different actions involve different objects in image sequences and there is one-to-one relation between object and action type, we present a Bayesian network based framework which combines motion patterns and object usage information to recognize actions/objects. More specifically, our approach recognizes high-level actions and the related objects without any body-part segmentation, hand tracking, and temporal segmentation methods. Additionally, we present a novel motion representation, based on 3D Haar-like features, which can be formed by depth, color, or both images. Our approach is also appropriate for object and action recognition where the involved object is partially or fully occluded. Finally, experiments show that our approach improves the accuracy of both action and object recognition significantly.