计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211200133-7.doi: 10.11896/jsjkx.211200133
马汉达, 殷达
MA Han-da, YIN Da
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44 fps左右,可以满足实时追踪的要求。
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