Perception Method of Abnormal Events in Rail Transit based on Multi-source Point Cloud Information

With the development of the rail transportation business, the perception of abnormal events in rail transportation has become particularly important. It is necessary to explore and innovate the technology by acquiring point cloud information through a new perception method - LiDAR(Light Detection and Ranging). In this paper, we propose a method of rail transit abnormal event sensing based on multi-source point cloud information. It can sense rail transit abnormal events in different areas of rail transit station lobby scenes. We use Matlab-based statistical filtering, plane fitting, and point cloud rotation to complete the pre-processing, and extract the valid point cloud data contained in the original point cloud. Then we use the voxel-centered difference method to perceive the dynamic targets in the rail transit environment. And we use the model of human characteristics and the deep learning method based on PointNet to complete the perception of human point cloud data in the dynamic targets. Finally, the human behavior patterns are discriminated based on the enclosing frame and skeleton extraction methods. The experimental results show that the proposed method can effectively sense the dynamic human targets in the station lobby scenes and provide data support for subsequent abnormal event detection.