This study investigated the automated tracking of multiple cows simultaneously using computer vision and deep learning. Video clips were collected in 2019 at Dairy Campus, where cows were housed in small groups (n=16). A systematic approach covering the true variability of barn circumstances eventually resulted in the selection of 159 frames that were annotated by drawing bounding boxes around each cow. These frames were used to retrain and test four You Only Look Once version 5 (YOLOv5) models to automatically detect cows. The weights of the best performing YOLOv5 model were used to parametrize the deep learning algorithm DeepSORT to track multiple cows simultaneously. This algorithm was applied to a 10 min timeframe of a randomly selected video clip and evaluated by computing the multi-object tracking accuracy, which was 92.8%. This outcome is a promising and essential step towards automated monitoring of individual behaviour of group-housed cows.
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges
EditorsR.F. Veerkamp and Y. de Haas
Published: 2022 Pages: 3364
eISBN: 978-90-8686-940-4
Book Type: Conference Proceedings
139. Tracking multiple cows simultaneously in barns using computer vision and deep learning
C. Kamphuis Related information
1Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, the Netherlands.
*Corresponding author: claudia. kamphuis@wur. nl
, I. Adriaens Related information*Corresponding author: claudia.
1Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, the Netherlands.
, W. Ouweltjes Related information1Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, the Netherlands.
, I. Hulsegge Related information1Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, the Netherlands.
Pages: 606 - 609
Published Online: February 09, 2023
Abstract: