Abstract
To ensure reliable environmental perception in the realm of autonomous driving, precise and robust multi-object tracking proves imperative. This study proposes an innovative approach to multi-object tracking by combining YOLOv9's sophisticated detection capabilities with an enhanced DeepSORT tracking algorithm, enriched through the integration of optical flow. In the proposed method, the YOLOv9 detector acutely identifies objects in input images, and these detected entities are subsequently transmitted to the optimized DeepSORT tracking algorithm. The principal contribution of this study lies in improving the Kalman filter measurement model within DeepSORT by incorporating robust local optical flow, thus adding a velocity dimension to the filter's update vector. This novel approach significantly improves tracking resilience in the face of occlusions, rapid movements, and appearance changes. Evaluations on MOT17 and KITTI show substantial improvement gains of 2.42%, 2.85%, and 1.84% for HOTA, MOTA, and IDF1, respectively, on MOT17, and 1.94% in MOTA and 2.09% in HOTA on KITTI. The proposed method particularly excels in managing scenarios involving dense traffic and light variations, which are recurrent problems in dynamic urban environments. This enhanced performance positions the proposed solution as an essential component of future perception architectures for autonomous vehicles, promising safer and more efficient navigation in the complex real world.
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