Object tracking with 3D LIDAR via multi-task sparse learning

Moving object tracking is a fundamental task for autonomous vehicles operating in urban areas. In this paper, a novel sparse learning based object tracking algorithm utilizing 3D LIDAR data is proposed. The 3D point clouds acquired from HDL-64E 3D LIDAR are first resampled on a virtual image plane, where the hypothesis of the targets is generated under the particle filtering framework. Four complementary features, i.e., normal orientation, depth, LBP and HOG, are extracted on each particle to describe the appearance of the candidates. Then a multi-task multi-cue sparse learning algorithm is employed to select the best candidate and realize the tracking of the object. To improve the robustness of the algorithm, the sparse learning framework is further enhanced by a specifically designed background filtering and occlusion detection mechanism. The experiments carried out on KITTI benchmark show promising object tracking performance, especially when handling complex tracking situations such as occlusion and posture change.