Crowd Counting Using Deep Learning in Edge Devices

2021 ◽  
Author(s):  
Zuo Huang ◽  
Richard Sinnott ◽  
Qiuhong Ke
Keyword(s):  
Deep Learning ◽  
Crowd Counting

scholarly journals Near Real-time Crowd Counting using Deep Learning Approach

2020 ◽  
Vol 171 ◽  
pp. 770-779
Author(s):  
Ujwala Bhangale ◽  
Suchitra Patil ◽  
Vaibhav Vishwanath ◽  
Parth Thakker ◽  
Amey Bansode ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Approach ◽  
Crowd Counting

Deep learning based crowd counting model for drone assisted systems

2021 ◽  
Author(s):  
Marcin Woźniak ◽  
Jakub Siłka ◽  
Michal Wieczorek
Keyword(s):  
Deep Learning ◽  
Crowd Counting ◽  
Counting Model

scholarly journals DGG: A Novel Framework for Crowd Gathering Detection

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 31
Author(s):  
Jianqiang Xu ◽  
Haoyu Zhao ◽  
Weidong Min ◽  
Yi Zou ◽  
Qiyan Fu
Keyword(s):  
Deep Learning ◽  
Local Area ◽  
Video Frame ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Counting Method ◽  
Crowd Counting ◽  
Stable Pattern ◽  
Crowd Density ◽  
Public Areas

Crowd gathering detection plays an important role in security supervision of public areas. Existing image-processing-based methods are not robust for complex scenes, and deep-learning-based methods for gathering detection mainly focus on the design of the network, which ignores the inner feature of the crowd gathering action. To alleviate such problems, this work proposes a novel framework Detection of Group Gathering (DGG) based on the crowd counting method using deep learning approaches and statistics to detect crowd gathering. The DGG mainly contains three parts, i.e., Detecting Candidate Frame of Gathering (DCFG), Gathering Area Detection (GAD), and Gathering Judgement (GJ). The DCFG is proposed to find the frame index in a video that has the maximum people number based on the crowd counting method. This frame means that the crowd has gathered and the specific gathering area will be detected next. The GAD detects the local area that has the maximum crowd density in a frame with a slide search box. The local area contains the inner feature of the gathering action and represents that the crowd gathering in this local area, which is denoted by grid coordinates in a video frame. Based on the detected results of the DCFG and the GAD, the GJ is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for the crowd gathering action. Experiments based on benchmarks show that the proposed DGG has a robust representation of the gathering feature and a high detection accuracy. There is the potential that the DGG can be used in social security and smart city domains.

Deep Learning for Crowd Counting: A Survey

2019 ◽  
Vol 1 (1) ◽  
pp. 17-28
Author(s):  
Tjeng Wawan Cenggoro
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Learning Models ◽  
Crowd Counting ◽  
Big Picture ◽  
Deep Learning Model

The growth of deep learning for crowd counting is immense in the recent years. This results in numerous deep learning model developed with huge multifariousness. This paper aims to capture a big picture of existing deep learning models for crowd counting. Hence, the development of novel models for future works can be accelerated.

scholarly journals Deep Learning-Based Crowd Scene Analysis Survey

2020 ◽  
Vol 6 (9) ◽  
pp. 95
Author(s):  
Sherif Elbishlawi ◽  
Mohamed H. Abdelpakey ◽  
Agwad Eltantawy ◽  
Mohamed S. Shehata ◽  
Mostafa M. Mohamed
Keyword(s):  
Deep Learning ◽  
Automatic System ◽  
Scene Analysis ◽  
Crowd Counting ◽  
Public Events ◽  
Analysis Methods ◽  
The Difference ◽  
Evaluation Metric ◽  
Crowded Scenes

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

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