Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation
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, ter...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1045 |
| _version_ | 1827354927137030144 |
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| author | Jiahong Li Xinkai Xu Zhuoying Jiang Beiyan Jiang |
| author_facet | Jiahong Li Xinkai Xu Zhuoying Jiang Beiyan Jiang |
| author_sort | Jiahong Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T04:00:43Z |
| format | Article |
| id | doaj.art-96407ea2e312415f8079672a3b78babe |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:00:43Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-96407ea2e312415f8079672a3b78babe2024-02-09T15:07:29ZengMDPI AGApplied Sciences2076-34172024-01-01143104510.3390/app14031045Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square EstimationJiahong Li0Xinkai Xu1Zhuoying Jiang2Beiyan Jiang3Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, ChinaBeijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, ChinaCollege of Robotics, Beijing Union University, Beijing 100027, ChinaBeijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, ChinaReal-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.https://www.mdpi.com/2076-3417/14/3/1045visual object trackingKalman filterautocovariance least-squares estimationbackground subtractionadaptive thresholding |
| spellingShingle | Jiahong Li Xinkai Xu Zhuoying Jiang Beiyan Jiang Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation Applied Sciences visual object tracking Kalman filter autocovariance least-squares estimation background subtraction adaptive thresholding |
| title | Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation |
| title_full | Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation |
| title_fullStr | Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation |
| title_full_unstemmed | Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation |
| title_short | Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation |
| title_sort | adaptive kalman filter for real time visual object tracking based on autocovariance least square estimation |
| topic | visual object tracking Kalman filter autocovariance least-squares estimation background subtraction adaptive thresholding |
| url | https://www.mdpi.com/2076-3417/14/3/1045 |
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