Development and validation of a deep learning–based system for nighttime behavioral monitoring in captive Asian elephants: A preliminary study https://doi.org/10.12982/VIS.2026.076
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Abstract
Concerns over the welfare of captive elephants in tourism highlight the importance of objective and non-invasive behavioral monitoring. This study developed and validated a method for automated classification of key elephant behaviors using nighttime CCTV footage. An experienced veterinarian categorized video recordings of two adult male elephants in Thailand into four categories: walking, standing, eating, and sleeping. Two convolutional neural network models, ResNet-18 and ResNet-50, were trained and tested using four-fold cross-validation. Both models achieved a high overall accuracy of more than 90 percent, and sleep was detected with the most reliability, with a sensitivity of 99 percent. These findings suggest that automated behavior recognition may be useful for continuous welfare monitoring in captive elephants. The approach provides a cost-effective and practical monitoring system that may help identify behavioral changes associated with discomfort or disease.
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