Abstract
In order to solve the problem of scale variation in Kernelized Correlation Filter (KCF) tracker, a scale adaptive tracking method based on Scale-Invariant Feature Transform (SIFT) is proposed. Firstly, it uses SIFT to extract and match keypoints between two successive frames to estimate the new scale of the target. Secondly, it utilizes keypoints information to resist strong disturbance of complex scenes, so that the method this paper proposes can be more robust. The method is tested in standard tracking library and compared with original tracking method in center location error and the overlap rate. These results illustrate that our tracking method with SIFT scale compensation improves the performance effectively.
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Acknowledgements
This work was supported by the NSFC (61327807, 61521091, 61520106010, 61134005) and the National Basic Research Program of China (973 Program: 2012CB821200, 2012CB821201)
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Qiao, X., Jia, Y. (2018). Scale Adaptive Kernelized Correlation Filter with Scale-Invariant Feature Transform. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_29
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DOI: https://doi.org/10.1007/978-981-10-6496-8_29
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