Abstract
We describe a practically evaluated approach for training visual cattle ID systems for a whole farm requiring only ten minutes of labelling effort. In particular, for the task of automatic identification of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other to significantly reduce the annotation requirements usually needed to train cattle identification frameworks. Evaluating the approach on the test portion of the publicly available Cows2021 dataset, for training we use 23,350 frames across 435 single individual tracklets generated by automated oriented cattle detection and tracking in operational farm footage. Self-supervised metric learning is first employed to initialise a candidate identity space where each tracklet is considered a distinct entity. Grouping entities into equivalence classes representing cattle identities is then performed by automated merging via cluster analysis and active learning. Critically, we identify the inflection point at which automated choices cannot replicate improvements based on human intervention to reduce annotation to a minimum. Experimental results show that cluster analysis and a few minutes of labelling after automated self-supervision can improve the test identification accuracy of 153 identities to 92.44% (ARI=0.93) from the 74.9% (ARI=0.754) obtained by self-supervision only. These promising results indicate that a tailored combination of human and machine reasoning in visual cattle ID pipelines can be highly effective whilst requiring only minimal labelling effort. We provide all key source code and network weights with this paper for easy result reproduction.
Original language | English |
---|---|
Title of host publication | 21st International Conference on Image Analysis and Processing Workshop (ICIAPW) on Learning in Precision Livestock Farming (LPLF) |
Publication status | Accepted/In press - 13 Apr 2022 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer Berlin Heidelberg |
ISSN (Print) | 0302-9743 |
Fingerprint
Dive into the research topics of 'Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification'. Together they form a unique fingerprint.Datasets
-
Cows2021 Dataset
Campbell, N. W. (Contributor), Burghardt, T. (Contributor), Gao, J. (Creator), Andrew, W. (Contributor) & Dowsey , A. (Contributor), University of Bristol, 15 Jun 2021
DOI: 10.5523/bris.4vnrca7qw1642qlwxjadp87h7, http://data.bris.ac.uk/data/dataset/4vnrca7qw1642qlwxjadp87h7
Dataset