Computer vision for zoo animals using YOLO algorithm / Muaz Wazir

For decades, zoo visit has been one of the best forms of educational visit someone could experience. Being able to visit exotic animals and learn about them, observing their activities and behavior, it’s the best way to experience nature's gift without risking the safety of visitors since the animals are usually in a protective enclosure (M. Wright, 2020). To better improve the experience of visiting a zoo, a field in Computer science and Artificial Intelligence is
suited for this improvement. Humans glance at an image and instantly know what objects are in the image, where they are, and how they interact. The human visual system is fast and accurate, allowing us to perform complex tasks like driving with little conscious thought. Current detection system repurposes classifiers to perform detection. (G.Plastiras, 2019). Computer vision is a scientific field that figures out how computers can gain high-level understanding from digital images and videos similar to how humans have eyesight. The discipline itself wishes to accomplish the process of automating tasks that the human visual system can do (J. Redmon, n.d). The idea behind the use of computer vision in this case is to provide a system with the ability to instantaneously recognize zoo animals and be able to use that knowledge to present the user with the appropriate information regarding the animals it
sees. This chapter provides the background and rationale for the study. The background of the study and the problem statement of this study will be discussed in this chapter. This section also explains the project's question, objective, scope and significance.