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HST-former: hierarchical spatio-temporal aggregation for video-based animal re-identification
Xiaolu Zhang1, Jiahao Wang2, Qingshuai Wang2
1Department of Information Engineering, Fujian Forestry Vocational & Technical College, Fujian, 353000, China. xiaoluzhangprf@yeah.net.
Scientific Reports
|April 9, 2026
Summary
This study introduces HST-Former, a novel framework for animal re-identification (Re-ID) using video data. It effectively utilizes dynamic information like gait, outperforming existing methods in wildlife conservation and research.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Wildlife Biology
Background:
- Video-based animal re-identification (Re-ID) is crucial for wildlife conservation and behavioral studies.
- Current image-based Re-ID methods do not fully leverage temporal dynamics like gait and motion patterns present in videos.
- There is a need for Re-ID methods that are data-efficient and species-agnostic, capable of processing rich video information.
Purpose of the Study:
- To propose HST-Former, a novel framework for high-precision, video-based animal Re-ID.
- To effectively utilize dynamic temporal information in videos for improved Re-ID accuracy.
- To develop a species-agnostic method that extends data efficiency into the temporal domain.
Main Methods:
- Developed the Hierarchical Spatio-Temporal Transformer Aggregator (HSTTA), a transformer architecture for processing animal trajectories.
- Modeled both spatial and temporal feature dependencies to learn long-range relationships and generate video-level descriptors.
- Implemented a hierarchical design to summarize intra-frame features and aggregate them across frames, addressing computational challenges.
- Introduced a spatio-temporal consistency constraint to enhance geometric verification and re-ranking accuracy.
Main Results:
- HST-Former significantly outperforms current state-of-the-art baselines in video-based animal Re-ID.
- Achieved the best performance across key metrics (Top-1, Top-3, Top-5) on three public datasets.
- Demonstrated the effectiveness of leveraging dynamic information and hierarchical feature aggregation.
Conclusions:
- HST-Former represents a significant advancement in video-based animal Re-ID by effectively integrating temporal dynamics.
- The proposed HSTTA architecture and spatio-temporal consistency constraint enhance discriminative power and re-ranking accuracy.
- The framework shows strong potential for applications in wildlife conservation and behavioral research due to its high precision and species-agnostic nature.
