基于实例分割与毕达哥拉斯模糊决策的目标跟踪  

Object tracking based on instance segmentation and Pythagorean fuzzy decision-making

作　　者：赵元龙 单玉刚 袁杰[1] 赵康迪 ZHAO Yuanlong;SHAN Yugang;YUAN Jie;ZHAO Kangdi(School of Electrical Engineering,Xinjiang University,Urumqi Xinjiang 830017,China;School of Education,Hubei University of Arts and Science,Xiangyang Hubei 441053,China)

机构地区：[1]新疆大学电气工程学院,乌鲁木齐830017 [2]湖北文理学院教育学院,湖北襄阳441053

出　　处：《计算机应用》2023年第6期1930-1937,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62263031);新疆维吾尔自治区自然科学基金资助项目(2022D01C53);湖北省教育科学规划基金资助项目(2021GA048);教育部产学合作协同育人项目(202102602033);襄阳市科技计划项目(高新领域)(2020ABH001799);湖北文理学院“双百行动计划”项目(PYSB20202016);湖北文理学院2021年度教师科研能力培育基金资助项目(2021KPGPSK08)。

摘　　要：为了解决目标跟踪中的尺度变化、相似性干扰、遮挡等问题,提出一种基于实例分割与毕达哥拉斯模糊决策的目标跟踪算法。在实例分割网络YOLACT++(improved You Only Look At CoefficienTs)的基础上,融合3种不同的匹配方式针对不同场景预测跟踪结果;同时提出一种基于毕达哥拉斯模糊决策的模板更新机制,即根据预测结果的质量作出是否更新目标模板和更换匹配方式的决定。实验结果表明,所提算法能够更准确地跟踪存在尺度变化、相似性干扰、遮挡等问题的视频序列。相较于SiamMask算法,所提算法在DAVIS 2016、DAVIS 2017数据集上的区域相似度分别提高了12.3、15.3个百分点,在VOT2016、VOT2018数据集上的预期平均重叠率(EAO)分别提高了4.2、4.1个百分点,且所提算法的平均跟踪速度为每秒32.00帧,满足实时性要求。In order to solve the problems of scale change,similarity interference and occlusion in object tracking,an object tracking algorithm based on instance segmentation and Pythagorean fuzzy decision-making was proposed.Based on the instance segmentation network YOLACT++(improved You Only Look At CoefficienTs),three different matching methods were integrated to predict the tracking results for different scenes.At the same time,a template update mechanism based on Pythagorean fuzzy decision-making was proposed by which whether to update the object template and replace the matching method was determined according to the quality of the prediction results.Experimental results show that the proposed algorithm can track the video sequences with scale change,similarity interference,occlusion and other problems more accurately.Compared with SiamMask algorithm,the proposed algorithm has the regional similarity on DAVIS 2016 and DAVIS 2017 datasets increased by 12.3 and 15.3 percentage points,respectively,and the Expected Average Overlap rate(EAO)on VOT2016 and VOT2018 datasets increased by 4.2 and 4.1 percentage points,respectively.Meanwhile,the average tracking speed of the proposed algorithm is 32.00 frames per second,meeting real-time requirements.

关 键 词：图像处理 目标跟踪 毕达哥拉斯模糊决策 实例分割 孪生网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]