ResnetCrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification

Estimating the number of people in highly clustered crowd scenes is an extremely challenging task on account of serious occlusion and non-uniformity distribution in one crowd image. Traditional works on crowd counting take advantage of different CNN like networks to regress crowd density map, and further predict the count. In contrast, we investigate a simple but valid deep learning model that concentrates on accurately predicting the density map and simultaneously training a density level classifier to relax parameters of the network to prevent dangerous stampede with a smart camera. First, a combination of atrous and fractional stride convolutional neural network (CAFN) is proposed to deliver larger receptive fields and reduce the loss of details during down-sampling by using dilated kernels. Second, the expanded architecture is offered to not only precisely regress the density map, but also classify the density level of the crowd in the meantime (MTCAFN, multiple tasks CAFN for both regression and classification). Third, experimental results demonstrated on four datasets (Shanghai Tech A (MAE = 88.1) and B (MAE = 18.8), WorldExpo’10(average MAE = 8.2), NS UCF_CC_50(MAE = 303.2) prove our proposed method can deliver effective performance.

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DGG: A Novel Framework for Crowd Gathering Detection

Electronics ◽

10.3390/electronics11010031 ◽

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.

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Estimation of Crowd Density from UAV Images based on Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37324 ◽

2021 ◽

Vol 9 (VIII) ◽

pp. 242-248

Author(s):

Sarita Chauhan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Unmanned Aerial Vehicles ◽

Crowd Counting ◽

Aerial Vehicles ◽

Crowd Density ◽

Ip Camera ◽

Crowd Monitoring ◽

Uav Images ◽

Crowd Surveillance

Crowd monitoring is necessary to improve safety and controllable movements to minimize risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event etc. In this current digital age mostly crowd monitoring still relies on outdated methods such as keeping records, using people counters manually, and using sensors to count people at the entrance. These approaches are futile in situations where people's movements are completely unpredictable, highly variable, and complex. Crowd surveillance using unmanned aerial vehicles (UAVs), can help us solve these problems. The proposed paper uses a UAV on which an IP Camera will be attached to get media, we then use a convolutional neural network to learn a regression model for crowd counting, the model will be trained extensively by using three widely used crowd counting datasets, ShanghaiTech part A and part B, UCF-CC 50 and UCF-QNRF.

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Proposal of a Monitoring System to Determine the Possibility of Contact with Confirmed Infectious Diseases Using K-means Clustering Algorithm and Deep Learning Based Crowd Counting

Korean Institute of Smart Media ◽

10.30693/smj.2020.9.3.122 ◽

2020 ◽

Vol 9 (3) ◽

pp. 122-129

Author(s):

Dongsu Lee ◽

ASHIQUZZAMAN A K M ◽

Yeonggwang Kim ◽

혜주 신 ◽

Jinsul Kim

Keyword(s):

Deep Learning ◽

Infectious Diseases ◽

Monitoring System ◽

Clustering Algorithm ◽

Crowd Counting

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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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Hybrid Graph Neural Networks for Crowd Counting

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6839 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11693-11700 ◽

Cited By ~ 2

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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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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