基于自适应模板更新和局部-全局策略的无人机场景下目标跟踪算法

Object Tracking Algorithm Based on Adaptive Template Update and Local-Global Strategy in UAV Scenario

基于孪生网络框架的跟踪算法在平衡精度和速度方面具有巨大的优势。然而在复杂无人机场景下的目标外观发生变化时,基于孪生网络的跟踪算法缺乏有效的更新机制来获取有效模板信息而导致目标漂移甚至目标丢失。此外,当目标丢失时,跟踪算法缺乏有效的搜索策略来提高在线更新的效率。针对以上问题,在SiamRPN的基础上,提出一种基于自适应模板更新和局部-全局策略的无人机场景下的跟踪算法,简称SiamATU。SiamATU采用自适应的在线策略,根据历史帧和当前帧累积的模板信息,可以准确预测后续帧中目标的位置。局部-全局策略可以有效地提高搜索目标的效率,以实现准确、高效的目标跟踪。

Tracking algorithms based on siamese network framework have great advantages in balancing accuracy and speed.However,when the appearance of the target changes in complex UAV scenarios,the siamese network-based track-ing algorithm lacks an effective update mechanism to obtain valid template information and leads to object drift or even tar-get loss.In addition,when the target is lost,the tracking algorithm lacks an effective search strategy to improve the effi-ciency of online updates.To address the above problems,based on SiamRPN,this paper proposes a tracking algorithm based on adaptive template update and local-global strategy for UAV scenarios,referred to as SiamATU.SiamATU adopts an adaptive online strategy to accurately predict the position of the target in subsequent frames based on the accumulated template information of historical frames and current frames.The local-global strategy can effectively improve the efficiency of searching for targets to achieve accurate and efficient target tracking.