文献类型：期刊文献

英文题名：Kalman Filter for Spatial-Temporal Regularized Correlation Filters

作者：Feng, Sheng[1];Hu, Keli[1];Fan, En[1];Zhao, Liping[1];Wu, Chengdong[2]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Northeastern Univ, Fac Robot Sci & Engn, Shenyang 110819, Peoples R China

年份：2021

卷号：30

起止页码：3263

外文期刊名：IEEE TRANSACTIONS ON IMAGE PROCESSING

收录：SCI-EXPANDED(收录号：WOS:000626322500006)、、EI(收录号：20210910011216)、Scopus(收录号：2-s2.0-85101763368)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61941601, Grant 61703280, Grant 61871289, and Grant 61662025, in part by the Research Foundation for Talented Scholars of Shaoxing University under Grant 20185001, in part by the Public Welfare Technology Research Project of Zhejiang Province, under Grant LGG19F020007, in part by the Natural Science Foundation of Zhejiang Province under Grant LY20F020011 and Grant LY19F020015, in part by the General Research Project of Zhejiang Provincial Department of Education under Grant Y201839944, and in part by the Public Welfare Technology Application Research Project of Shaoxing City under Grant 2018C10013.

语种：英文

外文关键词：Target tracking; Tracking; Visualization; Kalman filters; Correlation; Real-time systems; Clutter; Visual tracking; Kalman filter; spatial-temporal regularized correlation filters; stride length control method; discrete-time Kalman estimator

外文摘要：We consider visual tracking in numerous applications of computer vision and seek to achieve optimal tracking accuracy and robustness based on various evaluation criteria for applications in intelligent monitoring during disaster recovery activities. We propose a novel framework to integrate a Kalman filter (KF) with spatial-temporal regularized correlation filters (STRCF) for visual tracking to overcome the instability problem due to large-scale application variation. To solve the problem of target loss caused by sudden acceleration and steering, we present a stride length control method to limit the maximum amplitude of the output state of the framework, which provides a reasonable constraint based on the laws of motion of objects in real-world scenarios. Moreover, we analyze the attributes influencing the performance of the proposed framework in large-scale experiments. The experimental results illustrate that the proposed framework outperforms STRCF on OTB-2013, OTB-2015 and Temple-Color datasets for some specific attributes and achieves optimal visual tracking for computer vision. Compared with STRCF, our framework achieves AUC gains of 2.8%, 2%, 1.8%, 1.3%, and 2.4% for the background clutter, illumination variation, occlusion, out-of-plane rotation, and out-of-view attributes on the OTB-2015 datasets, respectively. For sporting events, our framework presents much better performance and greater robustness than its competitors.