Motion estimation is crucial to predict where other traffic participants will be at a certain period of time, and accordingly plan the route of the ego-vehicle. This paper presents a novel approach to estimate the motion state by using region-level instance segmentation and extended Kalman filter (EKF). Motion estimation involves three stages of object detection, tracking and parameter estimate. We first use a region-level segmentation to accurately locate the object region for the latter two stages. The region-level segmentation combines color, temporal (optical flow), and spatial (depth) information as the basis for segmentation by using super-pixels and Conditional Random Field. The optical flow is then employed to track the feature points within the object area. In the stage of parameter estimate, we develop a relative motion model of the ego-vehicle and the object, and accordingly establish an EKF model for point tracking and parameter estimate. The EKF model integrates the ego-motion, optical flow, and disparity to generate optimized motion parameters. During tracking and parameter estimate, we apply edge point constraint and consistency constraint to eliminate outliers of tracking points so that the feature points used for tracking are ensured within the object body and the parameter estimates are refined by inner points. Experiments have been conducted on the KITTI dataset, and the results demonstrate that our method presents excellent performance and outperforms the other state-of-the-art methods either in object segmentation and parameter estimate.
A novel approach is presented here to solve the problem of motion occlusion and motion edge blurring in the existing scene flow estimation. First instance segmentation and superpixels are combined to segment the target and other regions in fusion segmentation. The pixels in each block are then redistributed by the optical flow to ensure the motion of pixels in the subblocks is consistent. Moreover, the 3D motion of subblocks with sufficient pixels is estimated by the energy function, and the others are considered outliers. Finally, the Driving and the KITTI benchmarks are used to evaluate the proposed method. The results demonstrated that the fusion of segmentation and redistribution is positive for the estimation, and this method outperforms the other state‐of‐the‐art methods both qualitatively and quantitatively.
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