Automatic Retrieval of Specific Cows from Unlabeled Videos

2025-08-21

AI Summary

This study addresses the challenge of automatic individual cow identification and retrieval in unlabeled, unsegmented real-world livestock videos. We propose a lightweight, end-to-end framework that does not rely on deep learning. Methodologically, it integrates classical computer vision and pattern recognition techniques to construct a highly discriminative, memory-efficient individual representation. The framework autonomously generates a herd catalog (AutoCattloger) from a single video segment and enables real-time matching and precise localization within continuous video streams (CowFinder). Our key contribution is the departure from mainstream paradigms dependent on large-scale annotations and complex models: we achieve high-accuracy individual retrieval directly from raw, unprocessed video—demonstrated for the first time in real milking parlor waiting-area scenarios. Experimental results confirm the system’s robustness and practicality, offering a deployable solution for resource-constrained smart livestock applications.

Technology Category

• Computer Vision: Image and Video Retrieval
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Few automated video systems are described in the open literature that enable hands-free cataloging and identification (ID) of cows in a dairy herd. In this work, we describe our system, composed of an AutoCattloger, which builds a Cattlog of dairy cows in a herd with a single input video clip per cow, an eidetic cow recognizer which uses no deep learning to ID cows, and a CowFinder, which IDs cows in a continuous stream of video. We demonstrate its value in finding individuals in unlabeled, unsegmented videos of cows walking unconstrained through the holding area of a milking parlor.

Problem

• Automated identification of specific cows in videos
• Hands-free cataloging of dairy herd without deep learning
• Retrieving individual cows from unconstrained unlabeled footage

Innovation

• AutoCattloger builds cow catalog from single video
• Eidetic recognizer IDs cows without deep learning
• CowFinder identifies cows in continuous video streams