Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Robust Human Detection and Tracking System
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
3D-tracking of head and hands for pointing gesture recognition in a human-robot interaction scenario
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Propagation of Pixel Hypotheses for Multiple Objects Tracking
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Visual tracking of independently moving body and arms
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A real time vision-based hand gestures recognition system
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Visual estimation of pointed targets for robot guidance via fusion of face pose and hand orientation
Computer Vision and Image Understanding
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This paper presents a new approach for real time detection and tracking of human hands and faces in image sequences. The proposed method builds upon our previous research on color-based tracking and extends it towards building a system capable of distinguishing between human hands, faces and other skin-colored regions in the image background. To achieve these goals, the proposed approach allows the utilization of additional information cues including motion information given by means of a background subtraction algorithm, and top-down information regarding the formed image segments such as their spatial location, velocity and shape. All information cues are combined under a probabilistic framework which furnishes the proposed approach with the ability to cope with uncertainty due to noise. The proposed approach runs in real time on a standard, personal computer. The presented experimental results, confirm the effectiveness of the proposed methodology and its advantages over previous approaches.