Animal Classification Using Facial Image

Wildlife monitoring is an important tool for improving biodiversity conservation efforts, conducting ecological research, and managing human/wildlife interactions. Traditional methods of monitoring wildlife such as manual observations of live animals or using captured camera images usually take considerable time and resources, and therefore they are prone to human error. In this paper, we describe an array of proposed automated systems designed to enable real-time detection and identification of wild animals through captured images and video streams, utilizing deep learning techniques. For this work, we will use the YOLO (You Only Look Once) object detection algorithm in conjunction with specific computer vision techniques in order to detect and classify different animal species based on captured images and video streams. The model was trained using both annotated datasets and defined datasets containing multiple types of species (e.g., deer, elephants, tigers, leopards). The detection process includes extracting frames from a video stream and performing image preprocessing on the individual frames, applying the trained YOLO object detection model to localize (bounding box) and identify (classification) the species of the detected animal(s) from the individual frames based on a confidence score. In addition to species detection, we have also developed a multi-object tracking system that allows for consistent identification of an animal across different frames and prevents double counting of an animal. The experimental trials conducted using this system have demonstrated high detection rates and real-time performance in various environmental conditions, including motion blur, partially obscured individuals, and shadows and reflections of light. This detection system can be used to assist with the management of forest-related resources. Recommendations made by the authors include: 1) the ability to more effectively manage forest resources through increased knowledge of wildlife behavior; 2) improved training and collaborative efforts with other researchers may result in added benefits; and 3) forest managers can derive benefits from the implementation of this new technology.