Multi-Object Tracking Consistently Improves Wildlife Inference
Camera traps have become a common tool for wildlife monitoring efforts in ecological research and biodiversity conservation. Wildlife classification models have benefited from the increase in wildlife visual data. These models reach high levels of accuracy on curated, high-quality datasets. However, their performance remains sensitive to real-world environmental constraints. They often produce inconsistent predictions when performing inference on temporally coherent sequences. The predicted label for a single individual shifts rapidly between frames. This study exploits the temporal nature of camera-trap data to augment inferred predictions from a wildlife classification model. Specifical...
| Authors | Mufhumudzi Muthivhi, Jiahao Huo, Fredrik Gustafsson et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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