Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-Scale Gesture Recognition from Time-Varying Contours
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
An Integrated Framework for Image Segmentation and Perceptual Grouping
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
An Iterative Bayesian Approach for Digital Matting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Effect of silhouette quality on hard problems in gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Toward cinematizing our daily lives
Multimedia Tools and Applications
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This paper describes an accurate human silhouette extraction method as applied to video sequences. In computer vision applications that use a static camera, the background subtraction method is one of the most effective ways of extracting human silhouettes. However it is prone to errors so performance of silhouette-based gait and gesture recognition often decreases significantly. In this paper we propose two-step segmentation method: trimap estimation and fine segmentation using a graph cut. We first estimated foreground, background and unknown regions with an acceptable level of confidence. Then, the energy function was identified by focussing on the unknown region, and it was minimized via the graph cut method to achieve optimal segmentation. The proposed algorithm was evaluated with respect to ground truth data and it was shown to produce high quality human silhouettes.