PawPrint: Whose Footprints Are These? Identifying Animal Individuals by Their Footprints
This addresses the need for non-invasive pet identification to help reduce the millions of lost or stolen animals annually, though it is incremental as it builds on existing computer vision techniques.
The paper tackles the problem of identifying lost pets by introducing PawPrint and PawPrint+, the first publicly available datasets for individual-level footprint identification in dogs and cats, and benchmarks methods like CNNs and Transformers to evaluate performance across different conditions.
In the United States, as of 2023, pet ownership has reached 66% of households and continues to rise annually. This trend underscores the critical need for effective pet identification and monitoring methods, particularly as nearly 10 million cats and dogs are reported stolen or lost each year. However, traditional methods for finding lost animals like GPS tags or ID photos have limitations-they can be removed, face signal issues, and depend on someone finding and reporting the pet. To address these limitations, we introduce PawPrint and PawPrint+, the first publicly available datasets focused on individual-level footprint identification for dogs and cats. Through comprehensive benchmarking of both modern deep neural networks (e.g., CNN, Transformers) and classical local features, we observe varying advantages and drawbacks depending on substrate complexity and data availability. These insights suggest future directions for combining learned global representations with local descriptors to enhance reliability across diverse, real-world conditions. As this approach provides a non-invasive alternative to traditional ID tags, we anticipate promising applications in ethical pet management and wildlife conservation efforts.