Motion segmentation with event camera: N-patches optical flow estimation and Pairwise Markov Random Fields

This paper proposes the N-patches optical flow estimation-based motion compensation for motion segmentation using event camera. The requirement for prior knowledge of the scenario to cluster events into different motion types is addressed by the N-patches optical flow estimation. The framework of motion segmentation combines motion compensation and Pairwise Markov Random Fields, which not only realize the alignment of events in space but also enhance the spatial correlation between events. The proposed approach aims to maximize the contrast of the images of warped events. It does so by minimizing a specially designed energy function that jointly estimates motion parameters and discrete label items. The problem of selecting the optimal value for N^∗ when executing N-patches optical flow estimation is discussed in Section 4.2 of the experiments. Finally, the proposed approach is evaluated in several scenes involving high-speed motion, low-light conditions, occlusions, and multiple moving objects. The experimental results show that the proposed approach achieves better segmentation performance than the event-based motion segmentation by motion compensation.