Homeland security video surveillance system utilising the internet of video things for smart cities. Issue 4 (4th April 2021)

• Record Type: Journal Article
• Authors: Ismail, Yasser; Hammad, Mohamed; Darwich, Mahmoud; Elmedany, Wael
• Is Part Of: IET computers & digital techniques. Volume 15:Issue 4(2021)
• Page Start: 302
• Page End: 319
• Publication Date: 2021-04-04
• Subjects: Computers -- Periodicals; Digital electronics -- Periodicals; Computer engineering -- Periodicals; Computer architecture -- Periodicals; Computer organization -- Periodicals; 621.39
• DOI: 10.1049/cdt2.12014
• Languages: English
• ISSNs: 1751-8601

Abstract

Abstract: In the contemporary video surveillance system, there have been many efforts made to maintain high security and extend the coverage areas in most of the countries around the world. The deployment of many surveillance cameras and sensors capable of detecting abnormal and meaningful events on the territories' streets and airports is an aspect of internal security. There are two main problems that affect the homeland security system, and the security cameras and sensors are not enough to cover all areas in the country. This is because of the high cost of the video surveillance cameras and the sensor installations, and the non‐standardisation of security cameras and sensors manufacturing, which is due to the differentiated infrastructure of companies or organizations that provide home security. The authors introduce a design and hardware implementation of a motion estimation (ME) co‐processor that can be used for video surveillance cameras in homeland security. The proposed ME co‐processor, if adopted in video surveillance cameras, can be connected utilising an internet of video things infrastructure (IoVT). The proposed co‐processor is suited for high‐efficiency encoding video surveillance systems (H.265/HEVC). Furthermore, to reduce the memory I/O, data reuse Level A and Level B have been used in the proposed architecture while taking full advantage of the hardware resources. Moreover, an effective local memory has been used to reuse the data during the process of loading both the search area and the current block into the processing element array (PE array). The performance of the proposed architecture has been calculated using subjective and quantitative measures techniques and compared to the full search block‐based motion estimation (FSBB‐ME) algorithm. Moreover, the proposed architecture achieves a very high video resolution accuracy that is similar to the accuracy of the FSBB‐ME algorithm. Modelism‐version10.4a has been used for simulation and time verification testing proposes. The proposed ME co‐processor can be embedded in the compressing decompressing and high definition broadcast for video surveillance systems.