结合目标检测与特征匹配的多目标跟踪算法

针对多目标跟踪算法在遮挡频繁的场景下存在目标关联准确性低的问题,提出一种结合检测与特征匹配的多目标跟踪算法. 该算法引入检测精度较高的YOLOv5作为多目标跟踪的检测器,能够精准定位目标,有效提高跟踪精度;在面对目标间遮挡时,通过专门设计特征匹配模型提取更为细致的特征,能够有效降低跟踪时目标ID的切换次数.在MOT16数据集上对跟踪性能进行评估,结果表明:所提方法可以有效缓解目标遮挡,实现稳定跟踪.

Multiple Object Tracking Algorithm Based on Detection and Feature Matching

Aiming at the problem that the multiple object tracking algorithm(MOT)had low accuracy of target association in frequent occlusion scenes, an MOT algorithm based on detection and feature matching is proposed in this paper. This algorithm introduces YOLOv5 with high detection accuracy as a detector for MOT, which can accurately locate the target and effectively improve the tracking accuracy. In addition, a feature matching model is specially designed when facing the goals keep out. This can extract more detailed features and effectively reduce the ID switching numbers during tracking. The tracking feature is evaluated on the MOT16 dataset, and the results show that the proposed algorithm can effectively alleviate the occlusion of the target and achieve stable tracking.