Multiple object tracking in farmland based on fusion point cloud data

Abstract

Object detection and tracking in a farmland environment is greatly important to unmanned agricultural machinery navigation technology. Based on the existing methods of fusion point cloud acquisition and object detection, the multiple object tracking method was further researched. Constant velocity model was selected as the motion model, and state estimation was achieved using Kalman filter. Data association between the detectors and trackers was realized through an improved GNN method based on the cost of category conversion. A tracking state machine and two threshold mechanisms were proposed to avoid “combined explosion” disaster. Fusion point cloud data were used for motion judgment to achieve the real motion attributes and speed of the objects based on ground reference. As the results, the average static drift of the tracking method was less than 6.5 cm, the discrimination accuracy of static objects exceeded 75%. Besides, the multiple object tracking precision was less than 35 cm, and the multiple object tracking accuracy exceeded 85%. Therefore, the proposed multiple object tracking method could satisfy the requirements in the farmland environment.