文献类型：期刊文献

英文题名：Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation

作者：Li, Jiahong[1,2];Xu, Xinkai[1,2];Jiang, Zhuoying[2];Jiang, Beiyan[1,2,3]

第一作者：李佳洪

通讯作者：Jiang, BY[1];Jiang, BY[2];Jiang, BY[3]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100027, Peoples R China;[3]Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100027, Peoples R China;[3]corresponding author), Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2024

卷号：14

期号：3

外文期刊名：APPLIED SCIENCES-BASEL

收录：;Scopus(收录号：2-s2.0-85192480860);WOS:【SCI-EXPANDED(收录号:WOS:001160370800001)】；

基金：No Statement Available

语种：英文

外文关键词：visual object tracking; Kalman filter; autocovariance least-squares estimation; background subtraction; adaptive thresholding

摘要：Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation via the Kalman filter (KF) and noise covariance estimation using the ALS method. The proposed algorithm offers a robust and efficient solution to adapting the system model mismatches or invalid offline calibration, significantly improving the state estimation accuracy in VOT. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its real-time efficiency. Experimental validations on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion, reveal that the AKF-ALS algorithm outperforms a standard KF with fixed noise statistics.