用双视角视频提升水下小鱼多目标追踪精度
A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data
- 基于双视角视频融合，复用单视角追踪模型
- 相对准确率达47%，实现3D轨迹输出
- 适合生态学中鱼类行为研究与复杂环境追踪
计算机视觉中的多目标追踪（MOT）已取得显著进展，但水下环境中小型鱼类的追踪因复杂的三维运动和数据噪声而面临独特挑战。传统单视角MOT模型在此类场景中表现有限。本论文通过适配先进的单视角MOT模型FairMOT与YOLOv8，应用于水下鱼类检测与追踪，以支持生态学研究。核心贡献是构建了一个多视角框架，利用双目视频输入提升追踪精度与鱼类行为模式识别能力。在水下鱼类视频数据集上集成并评估这些模型，结果表明该方法相较单视角方案显著提升了追踪的精确性与可靠性。所提框架以47%的相对准确率检测鱼体，并通过立体匹配技术生成新颖的3D输出，为理解鱼类运动与交互提供了更全面的视角。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) in computer vision has made significant advancements, yet tracking small fish in underwater environments presents unique challenges due to complex 3D motions and data noise. Traditional single-view MOT models often fall short in these settings. This thesis addresses these challenges by adapting state-of-the-art single-view MOT models, FairMOT and YOLOv8, for underwater fish detecting and tracking in ecological studies. The core contribution of this research is the development of a multi-view framework that utilizes stereo video inputs to enhance tracking accuracy and fish behavior pattern recognition. By integrating and evaluating these models on underwater fish video datasets, the study aims to demonstrate significant improvements in precision and reliability compared to single-view approaches. The proposed framework detects fish entities with a relative accuracy of 47% and employs stereo-matching techniques to produce a novel 3D output, providing a more comprehensive understanding of fish movements and interactions
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