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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Advances and Trends in Real Time Visual Crowd Analysis.

Khalil Khan¹, Waleed Albattah², Rehan Ullah Khan²

¹Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan.

Sensors (Basel, Switzerland)

|September 10, 2020

Summary

This summary is machine-generated.

This review details real-time crowd analysis and management methods, highlighting challenges in unconstrained environments and the evolution from traditional algorithms to deep learning techniques for improved safety and event monitoring.

Keywords:

crowd detection crowd image analysis crowd management crowd monitoring deep learning

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Area of Science:

Computer Vision
Scene Analysis

Background:

Real-time crowd analysis is crucial for applications like people counting, event management, and disaster response.
Despite advancements, managing crowds in unconstrained, real-world conditions remains a significant challenge.

Purpose of the Study:

To provide a comprehensive review of state-of-the-art crowd analysis and management techniques.
To analyze methods applicable to both controlled and unconstrained environments.
To discuss the evolution of crowd analysis from foundational research to modern deep learning approaches.

Main Methods:

Review of seminal and recent research in crowd management and monitoring.
Analysis of algorithms for both controlled and unconstrained crowd scenarios.
Inclusion of deep learning-based methods as the latest advancements.

Main Results:

Detailed comparison of various crowd analysis and management methods.
Illustration of the advantages and disadvantages of existing techniques.
Identification of current limitations and future research directions.

Conclusions:

The review synthesizes current knowledge on crowd analysis and management.
It highlights the ongoing challenges, particularly in unconstrained settings.
The findings aim to advance research and practical applications in crowd analysis.