Abstract
The paper presents a concept of automatic video stream analysis which leads to the detection and tracking of specific objects, namely fish silhouettes that move in a water tank. It is one of the most important problems to be taken into consideration during the environmental studies. The paper includes mathematical principles related to adaptive background model and object classifier. The approach involves Gaussian Mixture Model for background elimination and foreground objects extraction, morphological operations on binary image masks, and some heuristics at the stage of fish detection. Finally, a preliminary discrimination between fish and no-fish objects is performed. Developed algorithm has been implemented as a working model and tested on benchmark data taken in the real environment.
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Forczmański, P., Nowosielski, A., Marczeski, P. (2015). Video Stream Analysis for Fish Detection and Classification. In: Wiliński, A., Fray, I., Pejaś, J. (eds) Soft Computing in Computer and Information Science. Advances in Intelligent Systems and Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-15147-2_14
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DOI: https://doi.org/10.1007/978-3-319-15147-2_14
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