基于改进Fairmot框架的多目标跟踪

Multi-object tracking based on improved Fairmot framework

针对复杂场景下目标之间遮挡造成跟踪精度降低的问题,提出基于Fairmot框架的多目标跟踪改进算法。将主干网特征图通过三重注意力机制进行维度间的信息交互产生注意力掩模,提高对目标的定位能力;行人重识别分支采用Circle Loss依据当前状态选择优化程度,提取更为精确的表观特征,区分不同目标对象。实验结果表明,在MOT15数据集上跟踪精度提升至62%,MT(Mostly Tracked)提升至358,身份切换降低68次,在发生遮挡的场景中拥有更出色的跟踪效果。

Aiming at the problem of reduced tracking accuracy caused by occlusion between targets in complex scenes,an improved multi-object trcking algorithm based on the Fairmot framework is proposed.The feature map of the backbone network is used for information interaction between dimensions through a triplet attention mechanism to generate an attention mask,which improves the positioning ability of the target.Person re-identification branch adopts Circle Loss to select the degree of optimization according to the current state,to extract more accurate appearance features and distinguish different target objects.The experimental results show that the tracking accuracy on the MOT15 data set is increased to 62%,the MT(Mostly Tracked)is increased to 358,and the identity switching is reduced by 68 times.It has a better tracking effect in the scene where occlusion occurs.