Identification of Animal through Footprint Analysis

The study aims to develop an efficient and accurate system for automatically detecting and classifying animal footprints in various environmental conditions. Leveraging the capabilities of YOLOv8, the proposed method demonstrates robustness in identifying diverse animal species based on their footprints. The research involves dataset curation, model training, and performance evaluation to validate the effectiveness of the developed system. Results indicate promising accuracy rates, showcasing the potential of YOLOv8 for wildlife monitoring and conservation efforts. This work contributes to the advancement of computer vision techniques in the field of ecological research and animal behaviour studies. We analyzed a dataset comprising images distributed across classes. To summarise, the methods and unique characteristics of the Animal Detection using Footprints project are explained in this abstract. In addition to advancing animal identification systems, the study emphasizes the value of uncertainty concerns in practical applications by departing from conventional deblurring techniques and adopting a probalistic model.