In this study, a novel visualization framework for fish behavior recognition based on Slowfast networks and spatial-temporal graph convolutional networks (ST-GCN) is proposed. The framework can directly recognize fish behaviors in continuous videos and classify fish states in cases of severe fish stacking. A self-constructed fish behavior dataset containing 10 single fish HD videos and 300 fish schooling video clips covering three action categories and two state categories was collected. The evaluation was performed on this behavioral dataset. The results show that the framework achieves accuracies of 95.00% and 88.61% for state recognition and action recognition, respectively, exceeding those of several benchmark methods. Robustness and generalization experiments, as well as fish feeding experiments, were also conducted to demonstrate the potential application of the framework for guiding smart feeding in real production activities. The framework provides a novel solution for fish behavior analysis in the visual domain and can be extended to other aquatic animals or scenarios.

中文翻译：

---

基于视觉的双网络利用时空几何特征有效解决鱼类重叠的鱼类行为识别

在这项研究中，提出了一种基于Slowfast网络和时空图卷积网络（ST-GCN）的鱼类行为识别的新型可视化框架。该框架可以直接识别连续视频中的鱼类行为，并在鱼群堆积严重的情况下对鱼类状态进行分类。收集了自建的鱼类行为数据集，包含 10 个单鱼高清视频和 300 个鱼群视频片段，涵盖三个动作类别和两个状态类别。评估是在此行为数据集上进行的。结果表明，该框架的状态识别和动作识别准确率分别达到95.00%和88.61%，超过了几种基准方法。还进行了鲁棒性和泛化实验以及鱼类饲养实验，以展示该框架在实际生产活动中指导智能饲养的潜在应用。该框架为视觉领域的鱼类行为分析提供了一种新颖的解决方案，并且可以扩展到其他水生动物或场景。