Abstract
We explore video tracking and classification in the context of real time marine wildlife observation. Among other applications it can help biologists by automating the process of gathering data, which is often done manually. In this paper we present a system to tackle the challenge of tracking and classifying fish in real time. We apply Background Subtraction techniques to detect the fish, followed by Feature Matching methods to track their movements over time. To deal with the shortcomings of tracking by detection we use a Kalman Filter to predict fish positions and a local search recovery method to re-identify fish tracks that are temporarily lost due to occlusions or lack of contrast. The species of tracked fish is recognized through Image Classification methods, using environment dependent features. We developed and tested our system using a custom built dataset, with several labeled image sequences of the fish tanks in the Oceanário de Lisboa. The impact of the proposed tracking methods are quantified and discussed. The proposed system is able to track and classify fish in real time in two scenarios, main tank and coral reef, reflecting different challenges.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Beauxis-Aussalet, E., Palazzo, S., Nadarajan, G., Arslanova, E., Spampinato, C., Hardman, L.: A video processing and data retrieval framework for fish population monitoring. In: Proceedings of the 2nd ACM International Workshop on Multimedia Analysis for Ecological Data (MAED), pp. 15–20 (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)
De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemometr. Intell. Lab. Syst. 50(1), 1–18 (2000)
Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 175–181 (1997)
Kavasidis, I., Palazzo, S.: Quantitative performance analysis of object detection algorithms on underwater video footage. In: Proceedings of the 1st ACM International Workshop on Multimedia Analysis for Ecological Data (MAED), pp. 57–60 (2012)
Kavasidis, I., Spampinato, C., Giordano, D.: Generation of ground truth for object detection while playing an online game: productive gaming or recreational working? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 694–699 (2013)
Nadeem, U., Shah, S.A.A., Bennamoun, M., Togneri, R., Sohel, F.: Real time surveillance for low resolution and limited-data scenarios: an image set classification approach. arXiv:1803.09470 (2018)
Spampinato, C., Chen-Burger, Y.-H., Nadarajan, G., Fisher, R.B.: Detecting, tracking and counting fish in low quality unconstrained underwater videos. In: Proceedings of the 3rd International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 514–519 (2008)
Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), vol. 2, pp. 28–31 (2004)
Acknowledgments
This work was supported by Fundação para a Ciência e a Tecnologia, under project UIDB/50021/2020, Project E-ARK3 and partially supported by FCT with the LARSyS - FCT Plurianual funding 2020–2023.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Castelo, J., Pinto, H.S., Bernardino, A., Baylina, N. (2020). Video Based Live Tracking of Fishes in Tanks. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-50347-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50346-8
Online ISBN: 978-3-030-50347-5
eBook Packages: Computer ScienceComputer Science (R0)