Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

TL;DR

The paper surveys fish tracking, counting, and behaviour analysis in digital aquaculture, integrating vision, acoustic, and biosensor modalities to provide a holistic view. It highlights how 2D/3D visual tracking, acoustic tagging, and various counting and behaviour-analysis methods interrelate, while noting a lack of unified evaluation standards and comprehensive datasets. It reveals cross-cutting opportunities in multimodal data fusion, deep learning, and edge computing, including the potential of LLMs/AGI to unify perception, reasoning, and decision support in aquaculture. The work emphasizes practical implications for real-world deployments, welfare considerations, and the need for standardized benchmarks to enable robust comparisons and scalable, ethical aquaculture monitoring systems.

Abstract

Digital aquaculture leverages advanced technologies and data-driven methods, providing substantial benefits over traditional aquaculture practices. This paper presents a comprehensive review of three interconnected digital aquaculture tasks, namely, fish tracking, counting, and behaviour analysis, using a novel and unified approach. Unlike previous reviews which focused on single modalities or individual tasks, we analyse vision-based (i.e. image- and video-based), acoustic-based, and biosensor-based methods across all three tasks. We examine their advantages, limitations, and applications, highlighting recent advancements and identifying critical cross-cutting research gaps. The review also includes emerging ideas such as applying multi-task learning and large language models to address various aspects of fish monitoring, an approach not previously explored in aquaculture literature. We identify the major obstacles hindering research progress in this field, including the scarcity of comprehensive fish datasets and the lack of unified evaluation standards. To overcome the current limitations, we explore the potential of using emerging technologies such as multimodal data fusion and deep learning to improve the accuracy, robustness, and efficiency of integrated fish monitoring systems. In addition, we provide a summary of existing datasets available for fish tracking, counting, and behaviour analysis. This holistic perspective offers a roadmap for future research, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and to promote their practical implementations in real-world settings.

Figures (13)

• Figure 1: Fish trajectory under different frame rates (This figure was adapted from delcourt2013video).
• Figure 2: Fish tracking method based on YoloV5 and SiamRPN++ (This figure was reproduced from wang2022real).
• Figure 3: Three methods to measure the 3-D position of a fish in an aquarium (This figure was adapted from delcourt2013video).
• Figure 4: Three-dimensional trajectory of multiple fish in water tank via multi video tracking (This figure was inspired by cheng2018obtaining with minor changes).
• Figure 5: Fish tracking method based on an acoustic tag (This figure was reproduced frommartinez2021large with minor changes).