Ego-Motion Compensated for Moving Object Detection in a Mobile Robot
This paper presents a moving object detection method using optical flow in an image obtained from an omnidirectional camera mounted in a mobile robot. The moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the ego-motion of the camera. To obtain the optical flow, image is divided into grid windows and affine transformation is performed according to each window so that conformed optical flows are extracted. Moving objects are detected as transformed objects are different from the previously registered background. In omnidirectional and panoramic images, the optical flow seems to be emerging on focus of expansion (FOE), on the contrary, it to be vanishing on focus of contraction (FOC). FOE and FOC vectors are defined from the estimated optical flow and used as reference vectors for the relative evaluation of optical flow. In order to localize the moving objects, histogram vertical projection is applied with specific threshold. The algorithm was tested in a mobile robot and the proposed method achieved comparable results with 92.37% in detection rate.
KeywordsMoving object detection Omnidirectional camera Mobile robot Ego-motion compensated
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