Abstract
Tracking an object with limited prior information regarding to its appearance is a challenging problem that attracts much attention. In this paper, we propose a speeded up visual tracker that is not only capable of long-term tracking but also of online tasks. The tracker treats object tracking as a binary classification problem between the object and background information. Usually, little information is available for training in real cases, which makes trackers with pre-defined distance metric to drift. To solve this problem, the proposed tracker adopts distance metric learning to update classifier after every frame for a more robust tracking result. We use dense SIFT feature to describe an object appearance and randomized principle component analysis (RPCA) to reduce the original feature space dimensionality. Additionally, a new partially-updated template library is proposed for a more robust tracking. The experiment results show that the proposed tracker performs preferable comparing to state-of-art trackers.
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Car1 is denoted as CarMany1 in this paper.
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Bolt is denoted as Bolt1 in this paper.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61501139 and 61371100), and the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.2013136).
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Sun, S., Kang, W., Liu, G. (2019). Speeded Up Visual Tracker with Adaptive Template Updating Method. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_333
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DOI: https://doi.org/10.1007/978-981-10-6571-2_333
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