Video surveillance systems-current status and future trends. (August 2018)

• Record Type: Journal Article
• Authors: Tsakanikas, Vassilios; Dagiuklas, Tasos
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 736
• Page End: 753
• Publication Date: 2018-08
• Subjects: Surveillance -- Computer vision -- Image analysis -- Analytics -- Image features -- Surveillance analytics -- Cloud Computing -- Fog Computing -- Edge Computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.11.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance.