Robust pedestrian multi-object tracking in the intelligent bus environment

Abstract

Abstract Pedestrian multi-object tracking algorithms aim to maintain identity information of pedestrians by comparing the similarity between trajectories and detections, predicting pedestrian motion trajectories. However, within the context of intelligent bus, challenges arise due to factors such as passenger growth and vehicle vibrations, rendering existing pedestrian multi-object tracking algorithms less accurate. Therefore, this paper proposes an intelligent bus terminal robust pedestrian multi-object tracking algorithm named pure motion (PM), which can consistently and stably track pedestrians. The proposed algorithm employs several key strategies. Firstly, it optimizes trajectory prediction by adapting the aspect ratio of the prediction box based on pedestrian movement, automatically adjusting its shape, and selecting velocity weight coefficients according to different tracking targets. Secondly, it decomposes the homography matrix to acquire motion components and correct predicted results under motion conditions. Subsequently, the algorithm leverages the similarity between detection results and trajectories to retain high-confidence detections, eliminating low-confidence ones associated with background, thereby reducing false negatives and enhancing trajectory coherence. Futhermore, the introduction of detection confidence into trajectory updates to enhances the precision of measurement noise. The proposed algorithm underwent testing in intelligent bus driving scenarios, including turns, waiting for traffic lights, emergency braking, and approaching bus stops. The tracking accuracy on the MOT17-13-val dataset reaches 81.8. The results demonstrate that PM significantly improves the robustness of pedestrian multi-object tracking algorithms in the environment of intelligent bus.