A Comparison of Different Learning Algorithms for Wildlife Detection and Classification in Animal Conservation Applications

In order to track animal movement patterns, habitat use, population demographics, human-wildlife conflict, vehicle-animal collisions, snare and poaching occurrences, and epidemics, it is essential to monitor wildlife. Over time, accidents between wildlife and vehicles have increased in frequency on both rural and hilly roads and highways. Road deaths are sometimes caused by wildlife crossing the road at inopportune moments. The identification and categorization of animals from picture and video sequences is a popular area of study. Current methods for wildlife monitoring depend on image processing. This study provides a reliable method for detecting wildlife and divides it into two categories: animal-human conflict and animal-vehicle collision. Different learning approaches are employed to classify the animals with the use of datasets and a live motion sensor. Applications for wildlife have employed machine learning and deep learning extensively. This article discusses a variety of learning techniques and offers the authors' answers. There may be a chance to identify and categories wildlife when it enters a living area, an agricultural region, or a roadway. Various authors provide various classification and identification strategies based on Camera trap and Dataset.