Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

作     者：Jinping Sun Dan Li Honglin Cheng 

作者机构：School of Information Engineering(School of Big Data)Xuzhou University of TechnologyXuzhou221018China 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：655-675页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Basic Science Major Foundation(Natural Science)of the Jiangsu Higher Education Institutions of China(Grant:22KJA520012) the Xuzhou Science and Technology Plan Project(Grant:KC21303,KC22305) the sixth“333 project”of Jiangsu Province 

主　　题：Complex scene instance-specific proposals correlation filter multi-time-space perception object tracking 

摘      要：Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background ***,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed *** the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing ***,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift *** accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is *** distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing *** results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.