Abstract
This paper presents a comparative study of several state-of-the-art target tracking algorithms, including conventional and deep learning ones, for low quality videos. A challenge video data set known as SENSIAC, which contains both optical and infrared videos at long ranges (1000 m–5000 m), was used in our investigations. It was found that none of the trackers can perform well under all conditions. It appears that the field of video tracking still needs some serious development in order to reach maturity.
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Acknowledgments
This research was supported by US Air Force under contract FA8651-17-C-0017. The views, opinions and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
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Kwan, C., Chou, B., Kwan, LY.M. (2018). A Comparative Study of Conventional and Deep Learning Target Tracking Algorithms for Low Quality Videos. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_60
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DOI: https://doi.org/10.1007/978-3-319-92537-0_60
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