Multi-Object Tracking Algorithm for Unmanned Vehicle Autonomous Driving Scene Based on Online Spatiotemporal Feature Correlation

Aiming at the problem that the multi-object tracking algorithm is difficult to accurately design the object feature model and data association algorithm in the process of unmanned vehicle autonomous driving, a multi-object tracking algorithm based on online spatiotemporal feature correlation for unmanned vehicle autonomous driving scene (MOTA-BOSFCFUVADS) is proposed. Firstly, the algorithm performs object detection on the training samples, calibrates the coordinates of the detection results in the time dimension and the coordinates of the space dimension, eliminates the detection results whose confidence is less than the set value, and eliminates the overlapping boundaries in the detection through non-maximum suppression. Secondly, we use Kalman filter to predict the position of the tracking object in the current frame, then build the feature model of the object in the time dimension and the space dimension respectively, and fuse the temporal feature model of the tracking object with the spatial feature model, thereby, the spatiotemporal feature model of the tracking object is obtained. Finally, the spatiotemporal feature response of the object in the current frame is detected online, and the spatiotemporal feature response is correlated with the spatiotemporal object feature model of the tracking object, and then the similarity metric matching matrix obtained by fusion is calculated, and the tracking is solved by using the Hungarian algorithm. The optimal correlation pair between the object historical trajectory and the detection response, and update the parameters of the object spatiotemporal feature model. In addition, we use the MOT2015 database to test the effectiveness of the algorithm. The results show that the proposed algorithm has better tracking performance than the other two algorithms, and can effectively track multiple object continuously in time and space.