一种基于视频的非侵入性野生动物个体识别方法：利用步态动力学
A non-invasive video-based method for individual identification of wildlife using gait dynamics
AI总结：
研究针对野生动物个体识别难题，提出基于视频的全自动方法。用深度时空表征学习，结合SAM3、ResNet18和VideoPrism模型，经微调提取特征，以余弦相似度比较步态特征实现识别，多物种实验验证了方法有效性。
摘要：
步态是一种独特行为特征，可实现非侵入性个体识别。基于步态的分析在人类中已广泛研究，但在野生动物中的应用因环境变异性和缺乏可扩展识别方法而受限。本文提出一种基于视频的全自动野生动物步态分析和个体识别管道，使用深度时空表征学习。实验证明该方法有效，为野生动物个体识别提供了可行的非侵入性方法。
英文摘要：
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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