Automated real-time video surveillance algorithms for SoC implementation: A survey

Ehab Salahat, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri, Andrzej Sluzek, Mohammad Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Numerous techniques and algorithms have been developed and implemented, primarily in software, for object tracking, detection and recognition. A few attempts have been made to implement some of the algorithms in hardware. However, those attempts have not yielded optimal results in terms of accuracy, power and memory requirements. The aim of this paper is to explore and investigate a number of possible algorithms for real-time video surveillance, revealing their various theories, relationships, shortcomings, advantages and disadvantages, and pointing out their unsolved problems of practical interest in principled way, which would be of tremendous value to engineers and researchers trying to decide what algorithm among those many in literature is most suitable to specific application and the particular real-time System-on-Chip (SoC) implementation.

Original languageBritish English
Title of host publication2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-83
Number of pages2
ISBN (Print)9781479924523
DOIs
StatePublished - 2013
Event2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013 - Abu Dhabi, United Arab Emirates
Duration: 8 Dec 201311 Dec 2013

Publication series

NameProceedings of the IEEE International Conference on Electronics, Circuits, and Systems

Conference

Conference2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period8/12/1311/12/13

Keywords

  • Background Subtraction
  • FPGA
  • Maximally Stable Extremal Regions
  • Real-Time Video Surveillance
  • Scale-Invariant Feature Transform
  • Speeded Up Robust Features

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