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
Silhouette Analysis-Based Gait Recognition for Human Identification
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
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
Effect of silhouette quality on hard problems in gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic background substitution using monocular camera and temporal foreground probability model
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Unusual Event Recognition for Mobile Alarm System
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
People Detection by a Mobile Robot Using Stereo Vision in Dynamic Indoor Environments
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
| Hi-index | 0.00 |
Background subtraction methods have been used to obtain human silhouettes for gesture and gait recognition. However, background subtraction in pixel units is prone to error which decreases recognition performance significantly. In this paper we propose a novel background subtraction method that extracts foreground objects in region units. Together with the background model, an object's color and movement information are used to obtain the effective region object likelihood. Then an adaptive region decision function determines the object regions. Also, the sequential version of Horprasert's algorithm[2] is presented.