Detection and tracking of chickens in low-light images using YOLO network and Kalman filter

• Published in: Neural computing & applications Vol. 34; no. 24; pp. 21987 - 21997
• Main Authors: Siriani, Allan Lincoln Rodrigues, Kodaira, Vanessa, Mehdizadeh, Saman Abdanan, de Alencar Nääs, Irenilza, de Moura, Daniella Jorge, Pereira, Danilo Florentino
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Centroids; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image classification; Image processing; Image Processing and Computer Vision; Kalman filters; Luminous intensity; Moving targets; Original Article; Poultry; Probability and Statistics in Computer Science; Tracking; Video; Deep learning; YOLO v4; Convolutional neural network; Precision livestock farming

Continuous monitoring of chickens’ movement on-farm is a challenge. The present study aimed to associate the modified YOLO v4 model with a bird tracking algorithm based on a Kalman filter to identify a chicken’s movement using low-resolution video. The videos were captured in grayscale using a top-v...