A comparative study of intelligent surveillance with big data: methods, strengths, weaknesses, and future directions

10.48129/kjs.splml.19081

Authors

  • Imad Q. Habeeb University of Information Technology and Communications http://orcid.org/0000-0003-4289-6450
  • Hanan N. Abdulkhudhur Directorate of Education Al-Karkh 2, Ministry of Education, Baghdad, Iraq
  • Zeyad Q. Habeeb University of Technology, Biomedical Engineering, Baghdad, Iraq

DOI:

https://doi.org/10.48129/kjs.splml.19081

Abstract

Various challenges in intelligent surveillance with big data have attracted researchers to propose and design methods and techniques to solve these challenges. These methods have become an important part of healthcare, education, travel, security, and geographic research in our cities. They can monitor vehicles, count people, predict the intensity of tropical cyclones and give advance warnings of erratic behaviors or actions. However, the expansion of complex scenarios and big video data produces many papers describing how this data can be processed, analyzed, saved, and retrieved faster. The progress of these new trends is insufficient for intelligent surveillance solutions with big data. Hence, this research covers the academic literature with a comparative study of intelligent surveillance methods with big data. The study identified 98 papers for review from 2017 to 2022, then only 22 of them were finally selected for comparison according to a set of inclusion criteria. The result obtained includes all major aspects of these methods, such as their methodologies, purposes, strengths, and future directions, over the past six years of research. The objective of this paper was achieved by practically investigating recent trends in intelligent surveillance and its sub-topics in our life, using big data analytics. I hope that this comparative study will be a challenge for researchers and graduate students to find the gaps faster and to overcome the drawbacks in the current intelligent surveillance methods using complex scenarios.

Published

27-06-2022

Issue

Section

Special Issue on Machin Learning (CS)