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This paper describes a novel RGB-D-based visual target tracking method for person-following robots. We enhance a single-object tracker, which combines RGB and depth information, by exploiting two different types of distracters. First set of distracters includes objects existing near-by the target, and the other set is for objects looking similar to the target. The proposed algorithm reduces tracking...
Robotic person-following is an essential component for natural human robot interaction. To follow a person, the robot should track the target person robustly and in real time. Object tracking algorithms in the computer vision field typically require abundant features and heavy computing power, and thus cannot be directly applied to person-following robots due to the problems arising in practical robotic...
This paper presents a person following robot equipped a RGB-D imaging sensor. We introduce three modules of a visual target tracking, target detection, and robot control. For the tracking module, distracters existing near-by the target are explicitly tracked to support target tracking. Preliminary tests showed that the robot robustly follows the target in uncontrolled environment with cluttered background...
This paper presents visual object tracking for handheld devices. Due to the low computation performance of handheld devices, the algorithms embedded on the devices are required to be performed with low computational consumption. The usability also should be considered at the same time. We adopted the drag-and-selection method to improve the user interactivity in the target object selection stage....
This video demonstrates a person following robot that uses one RGB-D camera. A depth assisted color tracking method robustly tracks a fast moving target. The robot detects front obstacles from a depth image and avoids them. A person detector also works when the target is lost by occlusion and obstacle avoiding.
A character segmentation algorithm for automatic license plate recognition is presented in this paper. Character regions are selected through binarization, connected component analysis, and character recognition. A blob analysis operation excludes noisy blobs, merges fragmented blobs, and splits clumped blobs. A character segmentation module achieved an accuracy rate of 97.2%. The recognition accuracy...
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