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
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
Real-time elliptical head contour detection under arbitrary pose and wide distance range
Journal of Visual Communication and Image Representation
MetroSurv: detecting events in subway stations
Multimedia Tools and Applications
A real-time vision-based framework for human-robot interaction
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
| Hi-index | 0.00 |
A vision based head tracking approach is presented, combining foreground information with an elliptical head model based on the integration of gradient and skin-color information. The system has been developed to detect and robustly track a human head in cluttered workshop environments with changing illumination conditions. A foreground map based on Gaussian Mixture Models (GMM) is used to segment a person from the background and to eliminate unwanted background cues. To overcome known problems of adaptive background models, a high-level feedback module prevents regions of interest to become background over time. To obtain robust and reliable detection and tracking results, several extensions of the GMM update mechanism have been developed.