Classification of Animal Behaviour Using Deep Learning Models

Damage to crops by animal intrusion is one of the biggest threats to crop yield. People who stay near forest areas face a major issue with animals. The most significant task in deep learning is animal behaviour classification. This article focuses on the classification of distinct animal behaviours such as sitting, standing, eating etc. The proposed system detects animal behaviours in real time using deep learning-based models, namely, convolution neural network and transfer learning. Specifically, 2D-CNN, VGG16 and ResNet50 architectures have been used for classification. 2D-CNN, «VGG-16» and «ResNet50» have been trained on the video frames displaying a range of animal behaviours. The real time behaviour dataset contains 682 images of animals eating, 300 images of animas sitting and 1002 images of animals standing, therefore, there is a total of 1984 images in the training dataset. The experiment shows good accuracy results on the real time dataset, achieving 99.43 % with Resnet50 compared to 2D CNN ,VGG19 and VGG166.