摘要
Abstract
Transformer-based object tracking methods are widely used in the field of computer vision and have achieved excellent results.However,object transformations,object occlusion,illumination changes,and rapid object motion can change object information during actual tracking tasks,and consequently,the underutilization of object template change information in existing methods prevents the tracking performance from improving.To solve this problem,this paper presents a Transformer object tracking method,TransTRDT,based on real-time dynamic template update.A dynamic template updating branch is attached to reflect the latest appearance and motion state of an object.The branch determines whether the template is updated through the template quality scoring header;when it identifies the possibility of an update,it passes the initial template,the dynamic template of the previous frame,and the latest prediction after cropping into the dynamic template updating network to update the dynamic template.As a result,the object can be tracked more accurately by obtaining a more reliable template.The tracking performance of TransTRDT on GOT-10k,LsSOT,and TrackingNet is superior to algorithms such as SwinTrack and StarK.It outperforms to achieve a tracking success rate of 71.9％on the OTB100 dataset,with a tracking speed of 36.82 frames per second,reaching the current leading level in the industry.关键词
目标跟踪/注意力机制/动态模板更新/质量评分头/Transformer目标跟踪Key words
object tracking/attention mechanism/dynamic template update/quality scoring header/Transformer object tracking分类
信息技术与安全科学
