Abstract
Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.
Highlights
Hajj has been used as an opportunity for certain rituals
We propose a framework for crowd counting based on convolutional neural networks (CNNs) in this study.[2]
This paper provides a new approach for crowd density estimation using a convolutional neural network
Summary
Hajj has been used as an opportunity for certain rituals. The Hajj is linked to the life of the Islamic prophet Muhammad, who lived in the seventh century AD, Muslims believe that the tradition of pilgrimage to Mecca dates all the way back to Abraham’s time.[1]. We propose a method for crowd analysis and density estimation using deep learning. The benefit of the Convolutional Neural Network (CNN) model is that it is superior than handcrafted features in identifying crowd-specific characteristics. We propose a framework for crowd counting based on convolutional neural networks (CNNs) in this study.[2] Our aim is to analyze the map of crowd videos and use visualization for cross-scene crowd analysis in unseen target scenes. This paper focuses on advances in crowd control study with an emphasis on high-density crowds, Hajj crowds. This research proposes an algorithm based on a Convolutional Neural Networks model for Hajj applications. Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods
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