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Learning to detect dynamic feature points

The detection of dynamic points on a moving platform is an important task to avoid a potential collision. However, it is difficult to detect dynamic points using only two frames, especially when various input data such as ego-motion, disparity map, and optical flow are noisy for computing the motion of points. In this paper, we propose a supervised learning-based approach to detect dynamic points in consideration of noisy input data. First of all, to consider depth ambiguity that proportionally increases according to the distance to the ego-vehicle, we divide the XZ-plane (bird-eye view) into several subregions. Then, we train a random forest for each subregion by constructing motion vectors computed based on two motion metrics. Here, in order to reduce errors of the input data, the motion vectors are filtered based on a pairwise planarity check and then filtered motion vectors are used for training. In the experiments, the proposed method is verified by comparing the detection performance with that of previous approaches on the KITTI dataset.