Fish Tracking

Fish tracking research aims to accurately monitor fish movement and behavior in various environments, from aquaculture facilities to open oceans, using automated methods to overcome limitations of manual observation. Current efforts focus on developing robust and efficient algorithms, often employing deep learning architectures like transformers and DETR, to address challenges such as occlusion, high similarity between individuals, and unreliable detections in complex underwater scenes. These advancements are crucial for optimizing aquaculture practices, understanding fish ecology and behavior, and improving the efficiency of autonomous underwater vehicles for scientific research. The development of standardized datasets and evaluation metrics is also a key area of focus to facilitate comparison and progress in the field.