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

2016 ◽  
Vol 30 (08) ◽  
pp. 1655025 ◽  
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.

scholarly journals Crowd density estimation using deep learning for Hajj pilgrimage video analytics

F1000Research ◽  
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.

scholarly journals Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3777
Author(s):  
Yani Zhang ◽  
Huailin Zhao ◽  
Zuodong Duan ◽  
Liangjun Huang ◽  
Jiahao Deng ◽  
...  
Keyword(s):  
Density Estimation ◽  
State Of The Art ◽  
Spatial Context ◽  
Context Information ◽  
Crowd Counting ◽  
Multi Scale ◽  
Context Learning ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Density Results

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

scholarly journals Crowd density estimation using deep learning for Hajj pilgrimage video analytics

F1000Research ◽  
2022 ◽  
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 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.

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

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.

scholarly journals Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4855
Author(s):  
Gergely Csönde ◽  
Yoshihide Sekimoto ◽  
Takehiro Kashiyama
Keyword(s):  
Contextual Information ◽  
Semantic Segmentation ◽  
Estimation Methods ◽  
Crowd Counting ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Pedestrian Counting ◽  
Semantic Scene ◽  
Recognition Software ◽  
Improved Accuracy

Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semantic information, such as social roles (e.g., student, company worker, or police officer) or location-based roles (e.g., pedestrian, tenant, or construction worker). Some of these can be learned from the same set of features as the human nature of an entity, whereas others require wider contextual information from the human surroundings. The primary end-goal of developing recognition software is to involve them in autonomous decision-making systems. Therefore, it must be foolproof, which is, it must have good semantic understanding of the input. In this study, we focus on counting pedestrians in helicopter footage and introduce a dataset created from helicopter videos for this purpose. We use semantic segmentation to extract the required additional contextual information from the surroundings of an entity. We demonstrate that it is possible to increase the pedestrian counting accuracy in this manner. Furthermore, we show that crowd counting and semantic segmentation can be simultaneously achieved, with comparable or even improved accuracy, by using the same crowd counting neural network for both tasks through hard parameter sharing. The presented method is generic and it can be applied to arbitrary crowd density estimation methods. A link to the dataset is available at the end of the paper.

Crowd density estimation method based on floor area

2020 ◽  
Vol 1651 ◽  
pp. 012060
Author(s):  
Fujian Feng ◽  
Shuang Liu ◽  
Yongzheng Pan ◽  
Xin He ◽  
Jiayin Wei ◽  
...  
Keyword(s):  
Density Estimation ◽  
Estimation Method ◽  
Floor Area ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd Density Estimation Using Fusion of Multi-Layer Features

2020 ◽  
pp. 1-12
Author(s):  
Xinghao Ding ◽  
Fujin He ◽  
Zhirui Lin ◽  
Yu Wang ◽  
Huimin Guo ◽  
...  
Keyword(s):  
Density Estimation ◽  
Crowd Density Estimation ◽  
Crowd Density

scholarly journals Hybrid Graph Neural Networks for Crowd Counting

2020 ◽  
Vol 34 (07) ◽  
pp. 11693-11700 ◽  
Author(s):  
Ao Luo ◽  
Fan Yang ◽  
Xin Li ◽  
Dong Nie ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Network Architecture ◽  
Message Passing ◽  
Large Scale ◽  
State Of The Art ◽  
Density Variation ◽  
Feature Maps ◽  
Crowd Counting ◽  
Multi Scale ◽  
Crowd Density ◽  
Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

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