[Paper] Self-Supervised Animal Identification for Long Videos

Source: arXiv - 2601.09663v1

Overview

Identifying individual animals across long video recordings is a bottleneck for wildlife research, livestock monitoring, and behavioral studies. This paper presents a self‑supervised, memory‑efficient method that treats animal identification as a global clustering problem instead of a frame‑by‑frame tracking task. By requiring only bounding‑box detections and the known number of individuals, the approach achieves >97 % identification accuracy while fitting comfortably on a consumer‑grade GPU.

Key Contributions

• Global clustering formulation – reframes per‑frame tracking into a single clustering problem, eliminating temporal error accumulation.
• Self‑bootstrapping with Hungarian assignment – generates reliable pseudo‑labels on‑the‑fly using an optimal matching algorithm, enabling end‑to‑end learning without any identity annotations.
• Lightweight training pipeline – leverages a frozen pre‑trained backbone and a binary‑cross‑entropy loss adapted from vision‑language models, consuming < 1 GB GPU memory per batch (≈10× less than typical contrastive methods).
• State‑of‑the‑art performance – reaches >97 % identification accuracy on two challenging datasets (3D‑POP pigeon videos and 8‑calf feeding videos), matching or surpassing supervised baselines trained on >1 k labeled frames.
• Open‑source implementation – code and pretrained models released on Hugging Face for immediate reuse.

Methodology

1. Assumptions – each video contains a fixed, known number of animals (common in controlled experiments or enclosure monitoring). Only bounding‑box detections are needed.
2. Feature extraction – a frozen backbone (e.g., ResNet‑50 pre‑trained on ImageNet) processes each detected crop, producing a compact visual descriptor.
3. Pairwise sampling – random pairs of frames are drawn from the same video; their descriptors are concatenated and fed to a lightweight projection head.
4. Pseudo‑label generation – within each training batch, the Hungarian algorithm solves an optimal assignment between the projected descriptors and the known set of animal IDs, producing soft pseudo‑labels.
5. Loss function – a binary cross‑entropy loss (inspired by CLIP’s image‑text alignment) encourages the model to assign high similarity to correctly matched pairs and low similarity otherwise.
6. Clustering at inference – after training, descriptors from all frames are clustered (e.g., k‑means with k equal to the known number of animals) to obtain the final identity labels for the entire video.

The whole pipeline runs in a single forward‑backward pass per batch, avoiding the need to store long temporal histories.

Results & Findings

• Memory usage: < 1 GB GPU RAM per batch vs. 8–12 GB for typical contrastive self‑supervised trackers.
• Training speed: ~2× faster per epoch because the backbone is frozen and only a small projection head is updated.
• Robustness: Works well despite occlusions, varying lighting, and animal pose changes, thanks to the global clustering objective that leverages the entire video context.

Practical Implications

• Deployable on edge devices: Researchers can run the model on a laptop or a modest workstation without needing a high‑end GPU cluster.
• Eliminates annotation bottleneck: No need to manually label thousands of frames; a simple count of individuals and bounding boxes (obtainable from off‑the‑shelf detectors) suffices.
• Scalable to long recordings: Since the method does not maintain per‑frame state, it can process hours‑long videos without running out of memory.
• Integration with existing pipelines: The approach can be slotted after any object detector (YOLO, Faster‑RCNN, etc.) and before downstream behavior analysis tools, enabling automated identity‑aware ethograms.
• Potential cross‑domain use: The same clustering‑based self‑supervision could be adapted for other domains where the number of entities is known (e.g., tracking vehicles in a parking lot, monitoring robots on a factory floor).

Limitations & Future Work

• Fixed‑count assumption: The method requires the exact number of individuals beforehand; handling dynamic entry/exit of animals remains an open challenge.
• Dependence on detection quality: Poor bounding‑box accuracy degrades feature quality; integrating detection confidence into the clustering step could improve robustness.
• Limited to single‑camera setups: Extending the framework to multi‑camera networks (e.g., wide‑area wildlife monitoring) would require cross‑view association mechanisms.
• Future directions include learning to estimate the number of individuals on‑the‑fly, incorporating temporal cues for smoother identity transitions, and testing on more diverse species and outdoor conditions.

Authors

• Xuyang Fang
• Sion Hannuna
• Edwin Simpson
• Neill Campbell

Paper Information

• arXiv ID: 2601.09663v1
• Categories: cs.CV
• Published: January 14, 2026
• PDF: Download PDF