IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Particle Filter with Analytical Inference for Human Body Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Object Labelling from Human Action Recognition
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
A Theory of the Quasi-Static World
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Efficient search and verification for function based classification from real range images
Computer Vision and Image Understanding
Learning function-based object classification from 3D imagery
Computer Vision and Image Understanding
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The human-object interaction signatures approach to object recognition proposes to find and classify objects in a scene by referring solely to related human actions. This method specifically addresses the problems and opportunities encountered in the typical smart-home monitoring system: wide-angle views of cluttered scenes with frequent, repeated human activity. Traditional shape-based object recognition tends to fail under these conditions owing to the unconstrained variety of object shapes, target objects' low resolution, and the partial occlusion of target objects by other scene objects. In this new approach, the system labels objects using evidence accumulated over time and multiple instances of human-object interactions. Furthermore, it uses partial occlusions of the person by an object to refine the object label's position. Preliminary experiments with this approach have investigated interaction signatures associated with walking and sitting on a chair, and then used the detected signatures to label a sceneýs chairs and navigable floor space.