文献类型： 外文期刊
作者： Liu, Shengnan 1 ; Zhang, Jiapeng 1 ; Zheng, Haojun 1 ; Qian, Cheng 1 ; Liu, Shijing 1 ;
作者机构： 1.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China
2.Dalian Ocean Univ, Sch Nav & Naval Architecture, Dalian 116023, Peoples R China
3.Ocean Univ China, Sanya Oceanog Inst, Sanya 572011, Peoples R China
4.Chinese Acad Fishery Sci, East China Sea Fishery Res Inst, Shanghai 200090, Peoples R China
关键词： fish behavior; multi-object tracking; DeepSORT; low temperature stress
期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）
ISSN：
年卷期： 2025 年 13 卷 7 期
页码：
收录情况： SCI
摘要： Precise identification and quantification of fish movement states are of significant importance for conducting fish behavior research and guiding aquaculture production, with object tracking serving as a key technical approach for achieving behavioral quantification. The traditional DeepSORT algorithm has been widely applied to object tracking tasks; however, in practical aquaculture environments, high-density cultured fish exhibit visual characteristics such as similar textural features and frequent occlusions, leading to high misidentification rates and frequent ID switching during the tracking process. This study proposes an underwater fish object tracking method based on the improved DeepSORT algorithm, utilizing ResNet as the backbone network, embedding Deformable Convolutional Networks v2 to enhance adaptive receptive field capabilities, introducing Triplet Loss function to improve discrimination ability among similar fish, and integrating Convolutional Block Attention Module to enhance key feature learning. Finally, by combining the aforementioned improvement modules, the ReID feature extraction network was redesigned and optimized. Experimental results demonstrate that the improved algorithm significantly enhances tracking performance under frequent occlusion conditions, with the MOTA metric improving from 64.26% to 66.93% and the IDF1 metric improving from 53.73% to 63.70% compared to the baseline algorithm, providing more reliable technical support for underwater fish behavior analysis.
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