Next Article in Journal
Feasibility and Reliability of SmartWatch to Obtain 3-Lead Electrocardiogram Recordings
Next Article in Special Issue
Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning
Previous Article in Journal
Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing
Previous Article in Special Issue
Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller
Review

Advances and Trends in Real Time Visual Crowd Analysis

1
Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
2
Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
3
Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
4
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
5
Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5073; https://doi.org/10.3390/s20185073
Received: 2 August 2020 / Revised: 27 August 2020 / Accepted: 3 September 2020 / Published: 7 September 2020
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community. View Full-Text
Keywords: crowd image analysis; crowd monitoring; crowd management; deep learning; crowd detection crowd image analysis; crowd monitoring; crowd management; deep learning; crowd detection
Show Figures

Figure 1

MDPI and ACS Style

Khan, K.; Albattah, W.; Khan, R.U.; Qamar, A.M.; Nayab, D. Advances and Trends in Real Time Visual Crowd Analysis. Sensors 2020, 20, 5073. https://doi.org/10.3390/s20185073

AMA Style

Khan K, Albattah W, Khan RU, Qamar AM, Nayab D. Advances and Trends in Real Time Visual Crowd Analysis. Sensors. 2020; 20(18):5073. https://doi.org/10.3390/s20185073

Chicago/Turabian Style

Khan, Khalil, Waleed Albattah, Rehan U. Khan, Ali M. Qamar, and Durre Nayab. 2020. "Advances and Trends in Real Time Visual Crowd Analysis" Sensors 20, no. 18: 5073. https://doi.org/10.3390/s20185073

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop