The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2005 Papers
Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion overview of human actions
ACM SIGGRAPH Asia 2008 papers
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Assigning cameras to subjects in video surveillance systems
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
View-Independent Action Recognition from Temporal Self-Similarities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Viewpoint quality and scene understanding
VAST'05 Proceedings of the 6th International conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage
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In many scenarios a scene is filmed by multiple video cameras located at different viewing positions. The difficulty in watching multiple views simultaneously raises an immediate question - which cameras capture better views of the dynamic scene? When one can only display a single view (e.g. in TV broadcasts) a human producer manually selects the best view. In this paper we propose a method for evaluating the quality of a view, captured by a single camera. This can be used to automate viewpoint selection. We regard human actions as three-dimensional shapes induced by their silhouettes in the space-time volume. The quality of a view is evaluated by incorporating three measures that capture the visibility of the action provided by these space-time shapes. We evaluate the proposed approach both qualitatively and quantitatively.