Wildlife Monitoring and Surveillance

Animal detection and classifications using computer vision systems have been practically unusable in uncontrolled contexts for years, due to concerns about the accuracy of the algorithms used. As a result, object detection, recognition, and classification, among other things, are receiving a lot of attention. Visual monitoring of animals in their natural habitat is currently one of the most popular methods in the field of computer vision (CV) systems. However, real-time animal detection and classification methods are still unavailable. Deep Learning improvements in Computer Vision have been built and developed through time, primarily over one specific method - a Convolutional Neural Network(CNN). This project is proposed using CNN to detect and classify animals in digital images. The feature of the animal in the input image is extracted to aid decision making, which is then plugged into the classification part for model analysis. According to the experimental results, the proposed algorithm has a positive effect on overall animal classification performance. An accuracy of around 64% was achieved when the image size was 50 × 50..