## 1 Introduction  
Fish are important components of marine ecosystems and human culture and industry. Worldwide, 3 billion people eat them in large quantities [1]. Efforts must be made to monitor and manage fish populations and species to maintain connected registers and fish stocks [2].  
Modern aquaculture utilizes computer vision technology to analyze underwater images of fish, contributing to optimized water quality and improved production efficiency [3]. According to a 2014 report by the Food and Agriculture Organization (FAO), approximately 110 million tons of seafood are caught worldwide annually, providing about 20% of the animal protein intake for 3 billion people [4]. If overfishing due to global warming and ocean acidification continues, humans will no longer be able to consume seafood. In the long term, the best way to address seafood shortages and promote sustainable fishing is through aquaculture.  
According to a report by Statistics Korea [5], the number of marine aquaculture workers decreased from 6236 in 2011 to 5524 in 2019, as shown in Table 1, and most of the aquaculture workers are over 60 years old, resulting in a severe labor shortage [5]. By introducing ICTs (Information and Communications Technologies) into the marine aquaculture industry, labor-intensive primary industries can be transformed into IT industries,enhancing competitiveness. Traditional aquaculture processes rely heavily on visual observations by managers, making them highly dependent on subjective experience and requiring significant time and labor to digitize the observed results.  
Modern aquaculture mainly relies on sensors to acquire information about fish species, which is vast and complex, making it difficult to fully utilize. However, computer vision technology, based on image processing, pattern recognition, and artificial intelligence, is gradually advancing and can be widely applied in areas such as fisheries resource research, aquaculture, processing, and rare species protection through non-contact observation techniques [6]. Using computer vision technology to study underwater images of fish can enhance operational efficiency, refine data for aquaculture, optimize water quality, and achieve high production efficiency. Furthermore, this technology, combined with artificial intelligence, can enable intelligent task processing.  
Previous research has explored various approaches to fish detection and tracking. For instance, [PERSON] et al. proposed a feature learning and object recognition framework for underwater fish images [7], [PERSON] et al. studied methods to track multiple cues with long-term dependencies [8], [PERSON] et al. conducted research aimed at real-time multi-object tracking [9], and [PERSON] et al. explored object detection through deep layer aggregation [10]. And [PERSON] et al. conducted a study that integrated YOLOv3 and Mask-RCNN and YOLOv4 and Norfair tracking algorithms to track fish in video data. This integrated system continuously tracks the migration of the fish and is optimized to maintain high performance, especially in low-frame-rate video [11].  
These studies have contributed to improving the efficiency of fish detection and tracking using computer vision and artificial intelligence technologies.  
The purpose of this study is to efficiently detect and track underwater fish using Multiple Object Tracking (MOT) technology. To achieve this, we constructed a dataset focusing on rock bream, which is popular in Korean aquaculture, and utilized underwater video data collected from the internet in five different environments. The dataset was divided into Train and Test sets for training and evaluation. Each frame was labeled with the target fish object ID using the DarkLabel tool. Additionally, the FairMOT model was used to simultaneously implement pixel-level object detection and re-identification (Re-ID) functionality. We compared and analyzed two backbone models: FairMOT+YOLOv5s and FairMOT+DLA-34.