Spotted: 基于位置信息的相机陷阱调查中鬣狗和豹子的重新识别
Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys
AI总结：
提出Spotted框架，结合视觉相似性与时空可行性先验，减少专家审核量，在三个数据集上Top-5准确率提升最高9pp，查询比较量减少69pp。
摘要：
动物重新识别（ReID）在相机陷阱调查中仍然具有挑战性，由于图像质量低、光照和视角变化大以及每个个体的观测数量高度不平衡。因此，当前的ReID性能通常不足以实现完全自动化使用，实际工作流程通常依赖于专家对算法提出的候选匹配进行审查。此外，大多数现有方法几乎只关注视觉线索，而忽略了实地研究中常规可用的辅助信息，如图像时间戳和相机陷阱位置。我们引入了Spotted，一个基于位置信息、人在环中的动物ReID框架，该框架将视觉相似性与从相机位置导出的时空可行性先验相结合，从而减少了所需的专家审查量。我们的方法（i）基于两个检测对应同一个体所需的最小移动速度，计算一个与图像模型无关的可行性分数；（ii）使用这些可行性线索作为伪监督，在冻结的视觉基础模型之上训练一个轻量级头部；（iii）将调整后的视觉相似性与时空可行性融合，以获得鲁棒的成对匹配分数。我们还集成了一种主动对采样策略，通过优先对不确定的预测进行注释来加速标注。我们在三个具有挑战性的相机陷阱ReID数据集上评估了Spotted，这些数据集由斑点鬣狗和豹子组成，我们作为本工作的一部分发布。我们的模型在LeopardID102、SpottedHyenaID109和SpottedHyenaID415数据集上，平均Top-5识别准确率分别比最佳基线提高了9pp、2pp和9pp。此外，我们展示了我们的人在环策略在实现同等正匹配的同时，将查询比较次数减少了高达69pp。
英文摘要：
Animal re-identification (ReID) in camera-trap surveys remains challenging due to low image quality, strong variation in illumination and viewpoint, and highly imbalanced numbers of observations per individual. As a result, current ReID performance is often insufficient for fully automated use, and practical workflows typically depend on expert review of algorithmically proposed candidate matches. Moreover, most existing approaches focus almost exclusively on visual cues and overlook auxiliary information routinely available in field studies, such as image timestamps and camera-trap locations. We introduce Spotted, a location-informed, human-in-the-loop animal ReID framework that integrates visual similarity with spatio-temporal feasibility priors derived from camera locations, thereby reducing the amount of required expert review. Our method (i) computes an image-model-agnostic feasibility score based on the minimum travel speed required for two detections to correspond to the same individual, (ii) uses these feasibility cues as pseudo-supervision to train a lightweight head on top of a frozen visual foundation model, and (iii) fuses adapted visual similarity with spatio-temporal feasibility to obtain a robust pairwise matching score. We additionally integrate an active pair sampling strategy to accelerate annotation by initially prioritizing uncertain predictions. We evaluate Spotted on three challenging camera-trap ReID datasets comprised of spotted hyenas and leopards, which we release as part of this work. Our model improves average top-5 identification accuracy by 9pp, 2pp and 9pp over the best baseline on our LeopardID102, SpottedHyenaID109 and SpottedHyenaID415 datasets, respectively. Further, we show that our human-in-the-loop strategy reduces the number of queried comparisons by up to 69pp while achieving equivalent positive matches.
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