Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10049422/ |
| _version_ | 1797858195785383936 |
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| author | S. Shreesha M. M. Manohara Pai Ujjwal Verma Radhika M. Pai |
| author_facet | S. Shreesha M. M. Manohara Pai Ujjwal Verma Radhika M. Pai |
| author_sort | S. Shreesha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T21:09:22Z |
| format | Article |
| id | doaj.art-137a22ae8fa348fd8a8dca12aec489f5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T21:09:22Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-137a22ae8fa348fd8a8dca12aec489f52023-03-28T23:00:08ZengIEEEIEEE Access2169-35362023-01-0111294002941610.1109/ACCESS.2023.324714310049422Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)S. Shreesha0https://orcid.org/0000-0002-4552-5041M. M. Manohara Pai1https://orcid.org/0000-0003-2164-2945Ujjwal Verma2https://orcid.org/0000-0002-6133-5379Radhika M. Pai3https://orcid.org/0000-0002-0916-0495Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaAquaculture 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.https://ieeexplore.ieee.org/document/10049422/Pattern analysisfish behaviour analysisfish trackingfish trajectory dataset |
| spellingShingle | S. Shreesha M. M. Manohara Pai Ujjwal Verma Radhika M. Pai Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) IEEE Access Pattern analysis fish behaviour analysis fish tracking fish trajectory dataset |
| title | Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) |
| title_full | Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) |
| title_fullStr | Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) |
| title_full_unstemmed | Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) |
| title_short | Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid) |
| title_sort | fish tracking and continual behavioral pattern clustering using novel sillago sihama vid ssvid |
| topic | Pattern analysis fish behaviour analysis fish tracking fish trajectory dataset |
| url | https://ieeexplore.ieee.org/document/10049422/ |
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