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Adaptive feature matching network for object occlusion
- Optics and Precision Engineering Vol. 31, Issue 22, Pages: 3345-3356(2023)
- 作者机构：
1.大连民族大学 机电工程学院，辽宁 大连 116600
- 作者简介：
- 基金信息：
DOI：10.37188/OPE.20233122.3345
CLC：
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毛琳,苏宏阳,杨大伟.针对目标遮挡的自适应特征匹配网络[J].光学精密工程,2023,31(22):3345-3356.
MAO Lin,SU Hongyang,YANG Dawei.Adaptive feature matching network for object occlusion[J].Optics and Precision Engineering,2023,31(22):3345-3356.
毛琳,苏宏阳,杨大伟.针对目标遮挡的自适应特征匹配网络[J].光学精密工程,2023,31(22):3345-3356. DOI： 10.37188/OPE.20233122.3345.
MAO Lin,SU Hongyang,YANG Dawei.Adaptive feature matching network for object occlusion[J].Optics and Precision Engineering,2023,31(22):3345-3356. DOI： 10.37188/OPE.20233122.3345.
摘要
针对目标跟踪中常见的目标遮挡问题，提出一种自适应特征匹配网络。该网络通过计算查询帧与记忆帧像素级相似度，将目标和背景相似度关系进行编码，获得像素级相似度矩阵，并通过将查询帧与记忆帧分头的方式，实现多维度相似性计算，以关注查询帧中更多区域，并通过计算的相似度矩阵，对记忆帧进行自适应特征加权，以此来提高目标跟踪的精度和鲁棒性。此外，特征记忆网络可以对记忆帧进行挑选和保存，为特征匹配提供额外表观信息，使网络隐性学习目标运动趋势，进而实现更好的跟踪结果。实验结果表明，该方法在GOT-10k，LaSOT等数据集上表现良好，在GOT-10k数据集上，本文所提出的算法与STMTrack算法相比，,,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=50591471&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=50591470&type=,4.91066647,2.37066650,值提升1.8%。根据可视化结果显示，本文算法在目标遮挡、消失等挑战中，具有更强的鲁棒性。
Abstract
An adaptive feature-matching network is proposed to solve the common problem of object occlusion in object tracking. By calculating the pixel-level similarity between the query and memory frames， the network encodes the similarity relationship between an object and its background and obtains a pixel-level similarity matrix. By separating the query and memory frames， the network calculates the multi-dimensional similarity to focus on more areas in the query frame and adaptively weighs the memory frame through the calculated similarity matrix to improve the accuracy and robustness of object tracking. Additionally， the feature memory network selects and saves the memory frames， provides additional apparent information for feature matching， and allows the network to implicitly learn the moving trend of an object to achieve better tracking results. Experimental results show that this method performs well on GOT-10k， LaSOT， and other datasets. On GOT-10k datasets， compared with the STMTrack algorithm， the value of the proposed algorithm is improved by 1.8%. The visualization results show that the proposed algorithm is more robust in meeting the challenges of object occlusion and disappearance.
关键词
目标遮挡自适应特征匹配记忆网络
Keywords
object occlusionself-adaptionfeature matchingmemory network
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