Robust multiple obstacle tracking method based on depth aware OCSORT for agricultural robots

Abstract

Multiple Object Tracking (MOT) of dynamic obstacles in field is an important prerequisite for agricultural robots to achieve dynamic obstacle avoidance. The complex and unpredictable road environment in the rural areas will cause severe vibration to the robot, affecting the camera pose and consequently leading to object matching errors. Therefore, we propose an improved method named Depth Aware Observation Centric Simple Online and Realtime Tracking (DA-OCSORT) with two new modules named Inertial Measurement Unit (IMU)-based Camera Motion Compensation (ICMC) and Depth Aware (DA). This method can use IMU information to compensate for camera ego-motion and perform multi-dimensional matching through object depth information, thereby minimizing the influence of camera motion on tracking process. We created an open-source MOT dataset, named MOTorchard. Building upon the pure motion-based method OCSORT, we achieve SOTA on this MOTorchard with HOTA 54.1, MOTA 83.3, IDF1 52.9 and FPS 22.7. Moreover, the proposed two modules prove to have good real-time performance and strong adaptability to other published MOT algorithms.