Crowd Density Estimation Using Deep Learning for Hajj Pilgrimage Video Analytics

Bhuiyan, Md Roman; Abdullah, Junaidi; Hashim, Noramiza; Farid, Fahmid Al; Uddin, Dr Jia; Abdullah, Norra; Samsudin, Mohd Ali

doi:10.5281/zenodo.5635486

Cited by 2 publications

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“…To develop this model, we have used programming language python 3.6.15 with others libaries such as/opencv-python 3.4.11.43, NumPy 1.21.2, SciPy 1.21.2 and matplotlib 3.4.3. 32 We executed 30 frame extractions per second to assemble all of the footage into one clip. Feature extraction at different resolutions is the method used in spatial feature extraction.…”

Section: Methodsmentioning

confidence: 99%

“…Results from mapping were sent to the MSFRN where information fused across the scales and predictions were formed using boxes. 32 Finally, crowd density results were obtained by utilizing the Non-Maximum Suppression (NMS) which uses several resolutions in combination to arrive at the accurate result. After completing the whole process we got the crowd density result.…”

Section: Methodsmentioning

confidence: 99%

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Crowd density estimation using deep learning for Hajj pilgrimage video analytics

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Crowd density estimation using deep learning for Hajj pilgrimage video analytics

et al. 2022

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Crowd density estimation using deep learning for Hajj pilgrimage video analytics

et al. 2021

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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 paper aims to propose 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.

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Crowd Density Estimation Using Deep Learning for Hajj Pilgrimage Video Analytics

Cited by 2 publications

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Crowd density estimation using deep learning for Hajj pilgrimage video analytics

Crowd density estimation using deep learning for Hajj pilgrimage video analytics

Crowd density estimation using deep learning for Hajj pilgrimage video analytics

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