摘要
全卷积孪生神经网络SiameseFC有着追踪速度快、精度高等优势,但在较为复杂的场景下仍然存在一定的缺陷,并且模板不更新的追踪模式也会在快速变化下的场景中出现较大的误差。因此,提出了一种基于全卷积孪生神经网络的双模板异步更新的追踪算法。首先基于VGG-16网络提取深层与浅层两种特征,分别使用两套对应的模板,两套模板独立且异步地更新,从而节约计算资源。然后对于模板的更新,同时考虑初始模板、前一次追踪所用模板,以及前一帧追踪结果提取的模板,并且使用了基于APCE的判断机制,更新时动态地分配三者的比例。所提算法在OTB100的基准测试结果上优于SiamRPN和SiamDW等主流算法,成功率与精确度均提升了约4%~5%,并且速度达到了44fps左右,可以满足实时追踪的要求。
SiameseFC has the advantages of fast tracking speed and high accuracy,but it still has some defects in complex scenes,and the tracking mode without updating the template will also cause large errors in the scene that changes rapidly.Therefore,this paper proposes a new tracking method,the dual-template asynchronous update based on SiameseFC.Firstly,both the deep and shallow features are extracted from the VGG-16network,and two sets of corresponding templates are used respectively,the two sets of templates are updated independently and asynchronously to save computing resources.Then,for the update of the template,the initial template,the template used in the previous tracking,and the template extracted from the tracking result of the previous frame are considered at the same time.And it uses an APCE-based judgment mechanism to dynamically allocate the proportions of the three templatets when updating.This algorithm is superior to mainstream algorithms such as SiamRPN in the benchmark results of OTB100,the success rate and accuracy improve by about 4%~5%,and reaches about 44fps,which is sufficient to meet real-time tracking requirements.
作者
马汉达
殷达
MA Han-da;YIN Da(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期473-479,共7页
Computer Science
