Comparative Analysis of Different Approaches to Object Detection: A Survey

Abstract

Object detection has a pivotal role in the field of security. Often, a security breach occurs under cover of night when the visibility is reduced. Manual supervision is relatively difficult as visibility is drastically reduced and human error increases to a large extent. In recent times, automation in security has gained a lot of traction. There are various inventions made in the field of automated surveillance. There are various models to detect objects in a frame with high accuracy accurately. With the help of pre-defined datasets and exclusive object detection models following the concepts of deep learning, an automated surveillance system can be implemented with ease. This study discusses the impact of automated surveillance, both night vision and daytime surveillance, the pros and cons, and the development of automated surveillance models that lead to accurate results. The study mainly focuses on deep learning concepts used to improve object detection.