  3D moving object detection is one of the most critical tasks in dynamic scene
analysis. In this paper, we propose a novel Drosophila-inspired 3D moving
object detection method using Lidar sensors. According to the theory of
elementary motion detector, we have developed a motion detector based on the
shallow visual neural pathway of Drosophila. This detector is sensitive to the
movement of objects and can well suppress background noise. Designing neural
circuits with different connection modes, the approach searches for motion
areas in a coarse-to-fine fashion and extracts point clouds of each motion area
to form moving object proposals. An improved 3D object detection network is
then used to estimate the point clouds of each proposal and efficiently
generates the 3D bounding boxes and the object categories. We evaluate the
proposed approach on the widely-used KITTI benchmark, and state-of-the-art
performance was obtained by using the proposed approach on the task of motion
detection.
