Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07776 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917133093437440 |
|---|---|
| author | Schall, Maximilian Knöfel, Felix Leonard König, Noah Elias Kubeler, Jan Jonas von Klinski, Maximilian Linnemann, Joan Wilhelm Liu, Xiaoshi Schlegelmilch, Iven Jelle Woyciniuk, Ole Schild, Alexandra Wasmuht, Dante Espinet, Magdalena Bermejo Basas, German Illera de Melo, Gerard |
| author_facet | Schall, Maximilian Knöfel, Felix Leonard König, Noah Elias Kubeler, Jan Jonas von Klinski, Maximilian Linnemann, Joan Wilhelm Liu, Xiaoshi Schlegelmilch, Iven Jelle Woyciniuk, Ole Schild, Alexandra Wasmuht, Dante Espinet, Magdalena Bermejo Basas, German Illera de Melo, Gerard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring Schall, Maximilian Knöfel, Felix Leonard König, Noah Elias Kubeler, Jan Jonas von Klinski, Maximilian Linnemann, Joan Wilhelm Liu, Xiaoshi Schlegelmilch, Iven Jelle Woyciniuk, Ole Schild, Alexandra Wasmuht, Dante Espinet, Magdalena Bermejo Basas, German Illera de Melo, Gerard Computer Vision and Pattern Recognition 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 |
| title | GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.07776 |