Self-Supervised Animal Identification for Long Videos

TL;DR

This paper presents a highly efficient self-supervised animal identification method for long videos that achieves state-of-the-art accuracy with minimal memory usage, eliminating the need for manual annotations.

Contribution

It introduces a novel self-supervised approach that reframes animal identification as a global clustering task, enabling high accuracy with low resource requirements.

Findings

Achieves over 97% accuracy on real-world datasets.

Uses less than 1 GB GPU memory per batch, outperforming contrastive methods.

Matches or surpasses supervised models trained on over 1,000 labeled frames.

Abstract

Identifying individual animals in long-duration videos is essential for behavioral ecology, wildlife monitoring, and livestock management. Traditional methods require extensive manual annotation, while existing self-supervised approaches are computationally demanding and ill-suited for long sequences due to memory constraints and temporal error propagation. We introduce a highly efficient, self-supervised method that reframes animal identification as a global clustering task rather than a sequential tracking problem. Our approach assumes a known, fixed number of individuals within a single video -- a common scenario in practice -- and requires only bounding box detections and the total count. By sampling pairs of frames, using a frozen pre-trained backbone, and employing a self-bootstrapping mechanism with the Hungarian algorithm for in-batch pseudo-label assignment, our method learns discriminative features without identity labels. We adapt a Binary Cross Entropy loss from vision-language models, enabling state-of-the-art accuracy ($>$97\%) while consuming less than 1 GB of GPU memory per batch -- an order of magnitude less than standard contrastive methods. Evaluated on challenging real-world datasets (3D-POP pigeons and 8-calves feeding videos), our framework matches or surpasses supervised baselines trained on over 1,000 labeled frames, effectively removing the manual annotation bottleneck. This work enables practical, high-accuracy animal identification on consumer-grade hardware, with broad applicability in resource-constrained research settings. All code written for this paper are \href{https://huggingface.co/datasets/tonyFang04/8-calves}{here}.