RT Journal Article
T1 Fuzzy Logic in Surveillance Big Video Data Analysis
A1 Muhammad, Khan
A1 Obaidat, Mohammad S.
A1 Hussain, Tanveer
A1 Ser, Javier Del
A1 Kumar, Neeraj
A1 Tanveer, Mohammad
A1 Doctor, Faiyaz
AB CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers' attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.
SN 0360-0300
YR 2021
FD 2021-06
LK https://hdl.handle.net/11556/5191
UL https://hdl.handle.net/11556/5191
LA eng
NO Muhammad , K , Obaidat , M S , Hussain , T , Ser , J D , Kumar , N , Tanveer , M & Doctor , F 2021 , ' Fuzzy Logic in Surveillance Big Video Data Analysis ' , ACM Computing Surveys , vol. 54 , no. 3 , 3444693 . https://doi.org/10.1145/3444693
NO Publisher Copyright: © 2021 ACM.
NO This work is supported in part by PR of China Ministry of Education Distinguished Possessor Grant given to Prof. Obaidat under Grant No. MS2017BJKJ003. Javier Del Ser thanks the Basque Government for its funding support through the EMAITEK program. He also receives funding support from the Consolidated Research Group MATHMODE (Grant No. IT1294-19) granted by the Department of Education of the same institution. Updated author affiliations: KHAN MUHAMMAD: Visual Analytics for Knowledge Laboratory, Department of Software, Sejong University, Seoul 143-747, South Korea. TANVEER HUSSAIN Department of Software, Sejong University, Seoul 143-747, South Korea. NEERAJ KUMAR, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala and School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand Authors’ addresses: K. Muhammad (corresponding author), Department of Software, Sejong University, Seoul 143-747, South Korea; email: khanmuhammad@sju.ac.kr; M. S. Obaidat (corresponding author), Fellow of IEEE and Fellow of SCS, Founding Dean, College of Computing and Informatics, University of Sharjah 27272, UAE, KASIT, University of Jordan 11942, Jordan, and University of Science and Technology 100083, Beijing, China; msobaidat@gmail.com; T. Hussain, Intelligent Media Laboratory, Department of Software, Sejong University, Seoul 143-747, South Korea; email: anveer445@ieee.org; J. D. Ser, TECNALIA, Basque Research Technology Alliance (BRTA), 48160 Derio, Spain; and the University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; N. Kumar, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala 147004, Punjab, India; email: neeraj.kumar@thapar.edu; M. Tanveer, Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India; email: mtanveer@iiti.ac.in; F. Doctor, Centre for Computational Intelligence, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, United Kingdom; email: fdocto@essex.ac.uk.s. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 0360-0300/2021/05-ART68 $15.00 https://doi.org/10.1145/3444693
DS TECNALIA Publications
RD 28 sept 2024