Abstract
In order to solve the variation of target appearance and background influence to the visual tracking, we extend the robust fragment-based tracker to an adaptive tracker by selecting features with an online feature ranking mechanism, and the target model is updated according to the similarity between the initial and current models, which makes the tracker more robust. What is more, we reposition the integral histogram’s bin’s structure and that makes our tracker quicker. The proposed algorithm has been compared with fragment-based tracker, and the results proved that our method provides better performance.
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Acknowledgements
Project supported by the key program of the National Natural Science Foundation of China (Grant No. 61039003), the National Natural Science Foundation of China (Grant No. 41274038), the Aeronautical Science Foundation of China (Grant No. 20100851018), and the Aerospace Innovation Foundation of China (CASC201102).
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Huang, Y., Zhao, L. (2014). Robust Fragment-Based Tracking with Online Selection of Discriminative Features. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_58
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DOI: https://doi.org/10.1007/978-3-319-01766-2_58
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