Abstract
Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals from vast archives of camera trap footage. The primary obstacle to automating this process has been the lack of large-scale, "in-the-wild" video datasets suitable for training robust deep learning models. To address this gap, we introduce a comprehensive benchmark with three novel datasets: Gorilla-SPAC-Wild, the largest video dataset for wild primate re-identification to date; Gorilla-Berlin-Zoo, for assessing cross-domain re-identification generalization; and Gorilla-SPAC-MoT, for evaluating multi-object tracking in camera trap footage. Building on these datasets, we present GorillaWatch, an end-to-end pipeline integrating detection, tracking, and re-identification. To exploit temporal information, we introduce a multi-frame self-supervised pretraining strategy that leverages consistency in tracklets to learn domain-specific features without manual labels. To ensure scientific validity, a differentiable adaptation of AttnLRP verifies that our model relies on discriminative biometric traits rather than background correlations. Extensive benchmarking subsequently demonstrates that aggregating features from large-scale image backbones outperforms specialized video architectures. Finally, we address unsupervised population counting by integrating spatiotemporal constraints into standard clustering to mitigate over-segmentation. We publicly release all code and datasets to facilitate scalable, non-invasive monitoring of endangered species
Abstract (translated)
监测极度濒危的西部低地大猩猩目前受到大量手工劳动的影响，这些工作是为了从大量的相机陷阱视频资料中重新识别个体。自动处理这一过程的主要障碍是缺乏适合训练稳健深度学习模型的大规模、真实环境下的视频数据集。为了填补这一空白，我们引入了一个全面的基准，并提供了三个新的数据集：Gorilla-SPAC-Wild 是迄今为止最大的用于野外灵长类动物再识别的视频数据集；Gorilla-Berlin-Zoo 用于评估跨域再识别泛化能力；以及 Gorilla-SPAC-MoT，用于评估相机陷阱视频中的多目标跟踪性能。基于这些数据集，我们提出了 GorillaWatch，一个端到端的工作流程，整合了检测、追踪和再识别功能。 为了利用时间信息，我们引入了一种跨多个帧的自监督预训练策略，该策略利用轨迹的一致性来学习领域特定特征，而无需手动标签。为确保科学有效性，我们采用可微分的 AttnLRP 来验证我们的模型是否依赖于有鉴别性的生物标志特征而非背景相关因素。 广泛的基准测试随后证明，从大规模图像骨干网络中聚合特征优于专门针对视频架构的方法。最后，为了处理无监督的人口计数问题，我们将时空约束整合到标准聚类中以减少过度分割现象。 我们公开发布所有代码和数据集，以便为濒危物种提供可扩展、非侵入性的监测手段。
URL
https://arxiv.org/abs/2512.07776
