Abstract
Object tracking is an essential step of a video processing pipeline. In the Fish4Knowledge project, recognizing fish trajectories allows to provide information to higher-level modules, such as behavior understanding and population size estimation. However, video quality limitations and appearance/motion characteristics of fish make the task much more challenging than in typical human-based applications in urban contexts. To solve this problem, robust appearance and motion models must be employed: this chapter describes an approach devised to tackle the fish tracking problem in this project, and presents and evaluation of the tracking algorithm in comparison with state-of-the-art techniques.
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Giordano, D., Palazzo, S., Spampinato, C. (2016). Fish Tracking. In: Fisher, R., Chen-Burger, YH., Giordano, D., Hardman, L., Lin, FP. (eds) Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data. Intelligent Systems Reference Library, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-319-30208-9_10
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DOI: https://doi.org/10.1007/978-3-319-30208-9_10
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