Vision-based intelligent systems: State of the art and perspectives

Artificial Intelligence (AI) specifies simulation of human intelligence through machines to acquire the ability to act rationally like humans for solving problems. The design of an artificially intelligent system is based on various factors such as natural language processing (NLP), representation of knowledge, automated reasoning, machine and deep learning, computer vision, robotics etc. The area of computer vision (CV) focuses on reproducing parts of the complex human visual system and enables the computer systems to identify, analyze, understand, and process the objects present in an image like the human brain. There are various sub-domains of computer vision, such as object/event detection, object/activity identification, pose/motion estimation, and scene/image reconstruction etc. Object detection is a computer vision task employed to detect the instances of visual objects of a particular class in an image. There are different platforms available for achieving real-time object detection in different applications of intelligent systems. This paper surveys the computer vision implementation tasked across object detection using Machine Learning (ML) and Deep Learning (DL) algorithms for design of intelligent systems. The computer vision toolbox available in MATLAB is also very popular among researchers for its high utilization of advanced features for object detection and its tracking. This study provides a comprehensive summary of state-of-the-art technologies for implementation of computer vision and discusses the perspectives of applications in intelligent systems achieving real-time object detection in different domains.