We present the Binocular Sparse Feature Segmentation (BSFS) algorithm for vision-based person following with a mobile robot. BSFS uses Lucas-Kanade feature detection and matching in order to determine the location of the person in the image and thereby control the robot. Matching is performed between two images of a stereo pair, as well as between successive video frames. We use the Random Sample Consensus (RANSAC) scheme for segmenting the sparse disparity map and estimating the motion models of the person and background. By fusing motion and stereo information, BSFS handles difficult situations such as dynamic backgrounds, out-of-plane rotation, and similar disparity and/or motion between the person and background. Unlike color-based approaches, the person is not required to wear clothing with a different color from the environment. Our system is able to reliably follow a person in complex dynamic, cluttered environments in real time.
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