RAPID: Real-time Animal Pattern re-Identification on edge Devices

Abstract

Automatic re-identification of animals has significant potential for addressing pressing challenges of our times via population monitoring, individual health assessment or detailed behavior analysis. Although numerous computer vision based solutions have been proposed and many achieve high accuracy, those still remain unsuitable for real-time analysis and deployment on edge devices. Here, we introduce RAPID, an open-source1 algorithm for re-identifying patterned animal images at a rate exceeding 40-60 queries per second on standard PC or Laptop and over 10 queries per second on an inexpensive off-the-shelf edge device. RAPID operates efficiently in computationally restricted environments, relying solely on CPU, leaving GPU resources available for other tasks, all while maintaining state-ofthe-art accuracy. Our approach leverages SIFT descriptors, which continue to demonstrate competitive robustness and accuracy against recent traditional and deep-learning based methods. To counterpoise SIFTs primary limitations its high-dimensional feature vectors and the associated computational costs - we incorporate recent advancements in vector similarity search and construct a database of feature vectors rather than database images, further accelerating the search process. We evaluate our method, RAPID, on public re-identification datasets; additionally propose a RAPID-based tool, FalseTagFinder, for cleaning benchmark dataset labels and as a demonstration, provide corrections for the StripeSpotter dataset.