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Learning to Parse Pictures of People
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Recognizing and Tracking Human Action
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Filtering Using a Tree-Based Estimator
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Detecting Pedestrians Using Patterns of Motion and Appearance
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Recognizing Action at a Distance
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Fast Pose Estimation with Parameter-Sensitive Hashing
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Learning to track 3D human motion from silhouettes
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Bayesian Body Localization Using Mixture of Nonlinear Shape Models
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Recovering human body configurations: combining segmentation and recognition
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Inferring 3D body pose from silhouettes using activity manifold learning
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Real-Time Human Posture Reconstruction in Wireless Smart Camera Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
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ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
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Human pose recognition using chamfer distance in reduced background edge for human-robot interaction
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
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International Journal of Computer Vision
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We present a template-based approach to detecting human silhouettes in a specific walking pose. Our templates consist of short sequences of 2D silhouettes obtained from motion capture data. This lets us incorporate motion information into them and helps distinguish actual people who move in a predictable way from static objects whose outlines roughly resemble those of humans. Moreover, during the training phase we use statistical learning techniques to estimate and store the relevance of the different silhouette parts to the recognition task. At run-time, we use it to convert Chamfer distance to meaningful probability estimates. The templates can handle six different camera views, excluding the frontal and back view, as well as different scales. We demonstrate the effectiveness of our technique using both indoor and outdoor sequences of people walking in front of cluttered backgrounds and acquired with a moving camera, which makes techniques such as background subtraction impractical.