Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Dressed human modeling, detection, and parts localization
Dressed human modeling, detection, and parts localization
Fast 2D Hand Tracking with Flocks of Features and Multi-Cue Integration
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Style by demonstration: teaching interactive movement style to robots
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Design and evaluation techniques for authoring interactive and stylistic behaviors
ACM Transactions on Interactive Intelligent Systems (TiiS)
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The capability to follow a person in a domestic environment is an important prerequisite for a robot companion. In this paper, a tracking algorithm is presented that makes it possible to follow a person using a small robot. This algorithm can track a person while moving around, regardless of the sometimes erratic movements of the legged robot. Robust performance is obtained by fusion of two algorithms, one based on salient features and one on color histograms. Re-initializing object histograms enables the system to track a person even when the illumination in the environment changes. By being able to re-initialize the system on run time using background subtraction, the system gains an extra level of robustness.