Addressing the current Sustainable Development Goals of the UN, the issue of 'Life Below Water' has become a global concern. Expanding upon the research paper link provided, we have implemented real-time analysis to scale up our efforts. Recorded feed from the water bodies is being used to record observations of behavioral changes in aquatic life to analyze the rapid impact of human interference and environmental pollution. These observations serve as crucial indicators for local authorities, enabling them to take prompt action.
See requirements.txt
The raw dataset generated and/or analyzed during the present study is now available, under the repository name of Anomaly Behavior Recognition of Underwater Creatures Using Lite 3D Full-Convolution Network.
You can train your model with train.py
python train.py --output mix_fars_v04_result/
You can test our model for any given recorded video. Just add your video under the name "fishie.mp4" and run the command:
python test.py