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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Kwan, C., Chou, B., & Martin Kwan, L. Y. (2018). A comparative study of conventional and deep learning target tracking algorithms for low quality videos. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10878 LNCS, pp. 521–531). Springer Verlag. https://doi.org/10.1007/978-3-319-92537-0_60
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