Machine Learning Technique for Analyzing the Behavior of Fish in an Aquarium

Bhaskaran, R.; Baskaran, Rajesh Kanna; Vijayalakshmi, C.

doi:10.1007/978-981-33-6463-9_5

Cited by 3 publications

(3 citation statements)

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“…Their purpose is to aid the researchers to study climate change by analysing the behaviour of Sablefish. It is also observed from the literature that there exist several behaviour studies of the fishes in aquariums and tanks [32], [33], [34], [35], [36], [37], [38], [39]. However, these datasets are not available to the public domain for further studies.…”

Section: A Fish Tracking Datasetsmentioning

confidence: 99%

“…There exist several work in the literature concerning the fish behaviour study [32], [33], [34], [35], [36], [37], [38], [39]. Most common approach in the literature to study the behaviour of fish is to extract the trajectories and analyse it.…”

Section: Fish Behaviour Analysis Using Trackingmentioning

confidence: 99%

“…Most common approach in the literature to study the behaviour of fish is to extract the trajectories and analyse it. The studies mostly used zebra and tilapia fish species [14], [15], [38], [57], [30], [35]. It is interesting to note that these studies were conducted using tanks or aquarium [57], [58], [62].…”

Section: Fish Behaviour Analysis Using Trackingmentioning

confidence: 99%

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Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

et al. 2023

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Aquaculture provides food security to many developing countries and enhances the socio-economic conditions of the fishermen. To enhance the productivity of the aquaculture, it is necessary to maintain stress free controlled eco-system for the fishes. For recognising the stress in fishes, behaviour analysis of fishes via tracking is imperative. Early detection of stress in fish facilitates fishermen to take precautionary measures promptly. Computer vision-based fish behaviour analysis of economically important fish species is challenging due to the lack of datasets, occlusions, rapid changes in swim directions etc. The present study proposes a multiple fish video dataset of an economically important species in a controlled environment, namely Sillago Sihama-Vid with accurate annotations. The study emulates the natural environment of Sillago Sihama in a large aquarium. This work proposes a novel fish tracking algorithm that incorporates swim direction information in addition to temporal, appearance, and spatial information. The inclusion of swim direction information reduces the number of identity switches. Comparative performance analysis of the proposed tracking algorithm with the conventional methods on the developed dataset highlights the performance efficiency. The proposed method has a clear performance improvement in MOTA, MOTP, IDSW and MT with respect to the other compared methods. The study also presents a novel unsupervised continual behaviour modelling strategy to model the evolving behaviours of the fishes. Further, interpretation of fish behaviour from the proposed behaviour modelling is performed to highlight the reliability of the proposed method. The significance of the proposed method is that, it is independent of training and labelled data. In addition, the method represents an innovative alternative to capture all the non observable behaviours of the fishes. The proposed tracking and behaviour modelling strategy act as a benchmark for developing algorithms to study fish behaviour via tracking. Finally, the dataset provides an opportunity for developing computer vision-based models to analyse the different behaviours of fish Sillago Sihama.INDEX TERMS Pattern analysis, fish behaviour analysis, fish tracking, fish trajectory dataset.

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