基于运动信息和再匹配的多目标追踪

Multiple Object Tracking Based on Motion Information and Re-matching

随着目标检测模型的日趋成熟,基于检测的追踪成为多目标追踪研究的主要方向.借助几乎完美的目标检测结果,在数据关联时可以采用只使用IoU信息的方法.但是在实际使用中,少量丢失的检测会造成大量的身份互换和轨迹断裂,进而严重影响追踪效果.针对这一问题,该算法引入图像信息,使用IoU模型进行初步追踪,结合行人特征向量对初步追踪的结果进行校验,对没有通过校验的轨迹进行再匹配.对于目标间遮挡的问题,该算法采用预测目标的运动轨迹,提前采取措施的方法应对.该算法采用MOT16和2DMOT15数据集进行实验,均取得了较好的效果.该算法采用在线追踪模式,更适合在实际应用中使用.

With the maturity of object detection models,tracking-by-detection has become the mainstream of multi-object tracking research.Assisted by the almost perfect object detection results,data association can be formed only through the IoU information.However,in practice,a small number of missing detections will cause a large number of ID switches and fragmentations,which will seriously affect the tracking results.To solve this problem,the multiple object tracking algorithm is proposed with the introduction of image information.Specifically,preliminary tracking results obtained through the IoU model are verified with the pedestrian feature vector,and for the tracks that have not passed the verification,they are re-matched.For the problem of occlusion,the algorithm adopts the method of predicting the object trajectory and taking measures in advance.Tested on MOT16 and 2DMOT15 datasets,the algorithm has achieved good results,and it is more suitable for practical applications with its online tracking mode.