Double-Camera Fusion System for Animal-Position Awareness in Farming Pens

Double-Camera Fusion System for Animal-Position Awareness in Farming Pens

Blog Article

In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to 3 piece horse wall art the large scale of breeding and expensive labour.Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance.However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive.We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues.

This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered.Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training.The learned parameters are then used in a semi-supervised network and fine-tuned with a vista 5 vl5 small number of manually annotated landmarks.The actual pixel displacement error is introduced as a complement to an image similarity measure.

The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods.This approach also reduces the registration time of an unseen image pair to less than 0.5 s.The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis.