Crowd density estimation using deep learning for Hajj pilgrimage video analytics

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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Crowd density estimation using deep learning for Hajj pilgrimage video analytics

F1000Research ◽

10.12688/f1000research.73156.1 ◽

2021 ◽

Vol 10 ◽

pp. 1190

Author(s):

MD ROMAN BHUIYAN ◽

Dr Junaidi Abdullah ◽

Dr Noramiza Hashim ◽

Fahmid Al Farid ◽

Dr Jia Uddin ◽

...

Keyword(s):

Absolute Error ◽

Visual Surveillance ◽

Small Space ◽

Video Footage ◽

Crowd Control ◽

Crowd Density Estimation ◽

Crowd Density ◽

Precise Analysis ◽

Density Map ◽

Control Study

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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Multiscale Aggregate Networks with Dense Connections for Crowd Counting

Computational Intelligence and Neuroscience ◽

10.1155/2021/9996232 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Pengfei Li ◽

Min Zhang ◽

Jian Wan ◽

Ming Jiang

Keyword(s):

Mean Squared Error ◽

Absolute Error ◽

Image Features ◽

Convolutional Network ◽

Crowd Counting ◽

Squared Error ◽

Crowd Density ◽

Density Maps ◽

Density Map ◽

Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

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HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416550259 ◽

2016 ◽

Vol 30 (08) ◽

pp. 1655025 ◽

Cited By ~ 1

Author(s):

Nermin Kamal Abdel-Wahab Negied ◽

Elsayed B. Hemayed ◽

Magda Fayek

Keyword(s):

Hybrid Approach ◽

Thermal Characteristics ◽

Human Beings ◽

Crowd Counting ◽

Ambient Environment ◽

Crowd Control ◽

Crowd Density Estimation ◽

Crowd Density ◽

Environment Temperature ◽

Motion Characteristics

This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.

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Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2019070103 ◽

2019 ◽

Vol 10 (3) ◽

pp. 19-26

Author(s):

Syeda Ruheena Quadri

Keyword(s):

Image Processing ◽

Video Processing ◽

Measurement Techniques ◽

Processing Technique ◽

Research Field ◽

Controlling System ◽

Crowd Control ◽

Crowd Density Estimation ◽

Crowd Density ◽

Processing Techniques

Crowd control is needed to prevent the outbreak of disorder and prevent possible stampedes. An automated detection of people crowds from images has become a very important research field. Due to the importance of the topic, many researchers tried to solve this problem using CCTV street cameras. There are still significant problems in managing public pedestrian transport areas such as railway stations, stadiums, shopping malls, and religious gatherings. Using CCTV cameras, some image processing techniques are suitable for an automatic crowd monitoring system. The feasibility of such a system has been tested by analyzing the crowd behavior, crowd density and motion. Traditional measurement techniques, based on manual observations, are not suitable for comprehensive data collection of patterns of density and movement. Real-time monitoring is tedious and tiring, but critical for safety. The author has investigated a number of techniques for crowd density estimation, movement estimation, incident detection and their merits using image processing.

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On pixel count based crowd density estimation for visual surveillance

IEEE Conference on Cybernetics and Intelligent Systems, 2004. ◽

10.1109/iccis.2004.1460406 ◽

2005 ◽

Cited By ~ 8

Author(s):

Ruihua Ma ◽

Liyuan Li ◽

Weimin Huang ◽

Qi Tian

Keyword(s):

Density Estimation ◽

Visual Surveillance ◽

Crowd Density Estimation ◽

Crowd Density

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Recent survey on crowd density estimation and counting for visual surveillance

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2015.01.007 ◽

2015 ◽

Vol 41 ◽

pp. 103-114 ◽

Cited By ~ 97

Author(s):

Sami Abdulla Mohsen Saleh ◽

Shahrel Azmin Suandi ◽

Haidi Ibrahim

Keyword(s):

Density Estimation ◽

Visual Surveillance ◽

Recent Survey ◽

Crowd Density Estimation ◽

Crowd Density

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Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽

10.3390/drones5030068 ◽

2021 ◽

Vol 5 (3) ◽

pp. 68

Author(s):

Jiwei Fan ◽

Xiaogang Yang ◽

Ruitao Lu ◽

Xueli Xie ◽

Weipeng Li

Keyword(s):

Deep Learning ◽

Autonomous Navigation ◽

Detection Method ◽

Active Role ◽

Absolute Error ◽

Face Mask ◽

Learning Technology ◽

Flight System ◽

Crowd Density ◽

The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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