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Towards Optical Flow Ego-motion Compensation for Moving Object Segmentation

: Optical ﬂow is an established tool for motion detection in the visual scene. While optical ﬂow algorithms usually provide accurate results, they can not make a difference between image-space displacements originated from moving objects in the space and the ego-motion of the moving viewpoint. In the case of optical ﬂow-based moving object segmentation, camera ego-motion compensation is essential. Hereby, we show the preliminary results of a moving viewpoint optical ﬂow ego-motion ﬁltering method, using two dimensional optical ﬂow, image depth information and the camera holder robot arm’s state of motion. We tested its accuracy through physical experiments, where the camera was ﬁxed on a robot arm, and a test object was attached onto an other robot arm. The test object and the camera were moved relative to each other along given trajectories in different scenarios. We validated our method for optical ﬂow background ﬁltering, which showed 94.88% mean accuracy in the different test cases. Furthermore, we tested the proposed algorithm for moving object state of motion estimation, which showed high accuracy in the case of translational and rotational movements without depth variation, but lower accuracy, when the relative motion produced change in depth, or the camera and the moving object move in the same directions. The proposed method with future work including outlier ﬁltering and optimisation could become useful in various robot navigation applications and optical ﬂow-based computer vision problems.