Real-time Rigid Motion Segmentation using Grid-based Optical Flow

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In the paper, we propose a rigid motion segmentation algorithm with the grid-based optical flow. The algorithm selects several adjacent points among grid-based optical flows to estimate motion hypothesis based on a so-called entropy and generates motion hypotheses between two images, thus separates objects which move independently of each other. The grid-based entropy is accumulated as a new motion hypothesis generated and the high value of entropy means that the motion has been estimated inaccurately in the corresponding grid. The motion hypothesis is estimated by three-dimensional rigid transformation and classified by the open-source implementation of density-based spatial clustering of applications with noise (DBSCAN). For the evaluation of the proposed algorithm, we use a self-made dataset captured by ASUS Xtion Pro live RGB-D camera. Our algorithm implemented in the unoptimized MATLAB code spends 170 ms of average computational time per frame, showing the potential for the application to the robust real-time visual odometry.

Bibtex

@article{leereal,
	title={Real-time Rigid Motion Segmentation using Grid-based Optical Flow},
	author={Lee, Sangil and Kim, H Jin},
	booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
	year = {2017},
}

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