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Li X, Liu Q, Fan N, Zhou Z, He Z, Jing XY. Dual-regression model for visual tracking. Neural Netw 2020; 132:364-374. [DOI: 10.1016/j.neunet.2020.09.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/02/2020] [Accepted: 09/10/2020] [Indexed: 01/07/2023]
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Yue F, Li X. Improved kernelized correlation filter algorithm and application in the optoelectronic tracking system. INT J ADV ROBOT SYST 2018. [DOI: 10.1177/1729881418776582] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In order to improve the tracking accuracy and real-time performance of the optoelectronic tracking system, an improved kernelized correlation filter approach is developed to obtain precise tracking of a maneuvering object. The proposed strategy contains merits of adaptive threshold approach, kernelized correlation filter method, and Kalman filter algorithm. The adaptive threshold approach can choose the suitable threshold in accordance with the size of the target in the image to improve the tracking performance of the kernelized correlation filter method. When the change between previous position and current position is larger than the distance threshold, Kalman filter algorithm is used to predict the target position for tracking. The tracking accuracy of the proposed algorithm is improved by updating the prediction of the target position with a trusted algorithm. The experimental results on comparison with some state-of-the-art trackers, such as kernelized correlation filter; Tracking-Learning-Detection; scale adaptive with multiple features; minimum output sum of squared error; and dual correlation filter, demonstrate that the proposed approach has the effectiveness of tracking accuracy and real-time performance in tracking the maneuvering object.
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Affiliation(s)
- Fengfa Yue
- The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
| | - Xingfei Li
- The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
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Li X, Liu Q, He Z, Wang H, Zhang C, Chen WS. A multi-view model for visual tracking via correlation filters. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.09.014] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zhang L, Bi D, Zha Y, Gao S, Wang H, Ku T. Robust and fast visual tracking via spatial kernel phase correlation filter. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.10.131] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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You X, Zheng Y. An accurate and practical calibration method for roadside camera using two vanishing points. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.132] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Chen S, Li S, Ji R, Yan Y, Zhu S. Discriminative local collaborative representation for online object tracking. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.01.041] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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