A Dynamic MRF Model for Foreground Detection on Range Data Sequences of Rotating Multi-beam Lidar

摘要

In this paper, we propose a probabilistic approach for foreground segmentation in 360°-view-angle range data sequences, recorded by a rotating multi-beam Lidar sensor, which monitors the scene from a fixed position. To ensure real-time operation, we project the irregular point cloud obtained by the Lidar, to a cylinder surface yielding a depth image on a regular lattice, and perform the segmentation in the 2D image domain. Spurious effects resulted by quantification error of the dis-cretized view angle, non-linear position corrections of sensor calibration, and background flickering, in particularly due to motion of vegetation, are significantly decreased by a dynamic MRF model, which describes the background and foreground classes by both spatial and temporal features. Evaluation is performed on real Lidar sequences concerning both video surveillance and traffic monitoring scenarios.