Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
High-Speed Action Recognition and Localization in Compressed Domain Videos
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents an investigation into the design of a computer based human action recognition system aimed at localizing and recognizing moving objects in a controlled environment. A system based on the object identifier and shape descriptor techniques is proposed. Automated visual perception of the real world by computers requires classification of observed physical objects into semantically meaningful categories (such as ‘animal' or ‘person' or ‘objects'). This paper proposes a partially-supervised learning framework for classification of the moving objects especially vehicles and pedestrians that are detected and tracked in a variety of far-field video sequences, captured by a static camera. Introduction of scene-specific context features (such as image-position of objects using xml) is done to improve classification performance in any given scene. Along with this, a scene-invariant object annotation has been done to adapt this contextual model for new scenes.