BMCTrack-d：在复杂相机场景下通过背部标记实现猪的重识别与跟踪

中文摘要 AI 辅助

自动化猪监测对于评估猪的健康、行为和福利至关重要。迄今为止，大多数猪监测解决方案在群体层面运行，因为个体层面监测需要对每只猪进行可靠的长期识别与跟踪。对于家猪而言，这仍然具有挑战性，因为同一品种的猪通常外观高度一致。此外，猪监测研究几乎全部在俯视相机场景下开展，这种场景能大幅简化跟踪过程，但在实际应用中并非总能实现。本研究提出了BMCTrack-d，一种新颖的检测跟踪方法，该方法利用独特的背部标记，在存在猪快速移动、严重遮挡和低分辨率的复杂侧视相机场景中实现稳健的猪重识别与跟踪。该方法首先使用基于神经网络的背部标记分类器预测检测到的猪的身份；为了随时间提升重识别可靠性，引入了两个专用后处理阶段：时间预测一致性检查，即对照近期预测历史验证身份分配；以及去重，即解决每个时间步中冲突的身份分配。通过明确优先考虑基于外观的准确重识别而非连续跟踪，该方法解决了现有跟踪器在个体层面监测场景中的关键局限。在要求严苛的测试集上，BMCTrack-d在高阶跟踪准确率上分别优于两个强基线BoT-SORT-ReID和TrackTrack-ReID，幅度达9.11%和1.03%。这些结果证明了基于背部标记的重识别与跟踪在复杂场景中实现稳健个体层面猪监测的有效性。

英文摘要

Automated pig monitoring is essential for assessing their health, behaviour, and welfare. To date, most pig monitoring solutions operate on the group-level, because individual-level monitoring requires reliable long-term identification and tracking of each animal. For domesticated pigs this remains challenging because pigs of the same breed often have highly uniform appearances. Moreover, research on pig monitoring is almost exclusively reported in top-down view camera settings, which considerably ease tracking, but are not always an option in practice. In this work, BMCTrack-d is presented, a novel tracking-by-detection approach that leverages unique back marks to enable robust pig re-identification and tracking in a challenging side-view camera setting, afflicted by rapidly moving pigs, severe occlusions and low resolution. The method first predicts the detected pigs' identities using a neural network-based back mark classifier. To improve re-identification reliability over time, two dedicated post-processing stages are introduced: a temporal prediction consistency check, which validates the identity assignments against the recent prediction history, and deduplication, which resolves conflicting identity assignments in each time step. By explicitly prioritising accurate, appearance-based re-identification over continuous tracking, the proposed approach addresses a key limitation of existing trackers for individual-level monitoring scenarios. On a demanding test set BMCTrack-d outperforms two strong baselines, BoT-SORT-ReID and TrackTrack-ReID, by 9.11% and 1.03%, respectively, in higher-order tracking accuracy. These results demonstrate the effectiveness of back mark-based re-identification and tracking for robust individual-level pig monitoring in challenging settings.

发表机构

• University of Applied Sciences Upper Austria（上奥地利应用科学大学）
• TU Wien（维也纳技术大学）
• The University of Veterinary Medicine Vienna（维也纳兽医大学）

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