Intelligent Video Analytic for Suspicious Object Detection : A Systematic Review

Abstract

Conventional surveillance systems such as CCTV still have limitations that merely viewing and recording. This limitation causes its function to only be passive monitoring and unable to provide real-time early warning systems as an effort to anticipate security threats or violations of regulations. The increasing need in the security sector, especially in public area, requires a solution in the form of a system that can detect suspicious objects through video surveillance systems. The integration of artificial intelligent, machine learning, image processing and computer vision become the latest study in surveillance system development innovation. Although there are many datasets, methods and frameworks available in previous research, there are still few papers that discuss the use of intelligent video analytics in detecting suspicious objects. This paper will comprehensively and systematically review the literature on applying machine learning for object detection and video surveillance systems published between 2010 and 2020. The literature extraction process is carried out by identifying and analyzing papers to describe the scope of research to detect suspicious objects using intelligent video analytics, frameworks, methods, datasets and identifying suspicious characteristics. At the end of this paper, conclusions have been outlined regarding the challenges and opportunities for suspicious object detection research using video analytics in the future.