A non-invasive video-based method for individual identification of wildlife using gait dynamics

• URL: http://arxiv.org/abs/2607.04518v1
• Date: Sun, 05 Jul 2026 21:47:21 GMT
• Title: A non-invasive video-based method for individual identification of wildlife using gait dynamics
• Abstract summary: This paper presents a fully automated video-based pipeline for wildlife gait analysis and individual identification.<n>The proposed pipeline uses Segment Anything Model 3 (SAM3) to generate high-quality RGB and binary silhouette masks.<n>Cosine is then used to compare gait signatures, enabling similarity-based clustering of individuals without reliance on physical or invasive markings.
• Score: 21.468912995256698
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gait is a distinctive behavioral characteristic that enables non-invasive individual identification without requiring physical interaction with an animal. While gait-based analysis has been extensively studied in humans, its application to wildlife remains limited due to environmental variability and the lack of scalable identification methods. This paper presents a fully automated, video-based pipeline for wildlife gait analysis and individual identification using deep spatiotemporal representation learning. The proposed pipeline uses the Segment Anything Model 3 (SAM3) to generate high-quality RGB and binary silhouette masks, robustly isolating animals from complex natural backgrounds. Segmented video sequences are processed using a convolutional neural network (ResNet18) for spatial feature extraction and a transformer-based video model (VideoPrism) for temporal motion modeling. Both models are fine-tuned using a classification objective and subsequently used as feature extractors to generate discriminative gait representations. Cosine similarity is then used to compare gait signatures, enabling similarity-based clustering of individuals without reliance on physical markings or invasive tagging. Experiments conducted on multi-source wildlife video data across multiple species demonstrate strong intra-individual consistency and clear inter-individual separation. Quantitative results using cosine similarity distributions and silhouette scores confirm the effectiveness of the proposed method. These findings demonstrate that gait dynamics provide a viable, non-invasive approach for individual identification in wildlife and highlight the potential of video-based deep learning pipelines for scalable ecological monitoring.

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