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.

scholarly journals Challenges of Deep Learning for Crowd Analytics

2019 ◽  
Author(s):  
Muhammad Siraj
Keyword(s):  
Deep Learning ◽  
Large Datasets ◽  
Learning Models ◽  
Public Places ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Training Samples ◽  
Public Areas ◽  
Pros And Cons ◽  
And Training

In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.

scholarly journals Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4325
Author(s):  
Tiange Wang ◽  
Fangfang Yang ◽  
Kwok-Leung Tsui
Keyword(s):  
Deep Learning ◽  
Learning Technologies ◽  
Railway Track ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Learning Networks ◽  
Railway Transportation ◽  
One Stage ◽  
Input Size ◽  
Feature Pyramid

Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.

scholarly journals Comparing Deep Learning and Statistical Methods in Forecasting Crowd Distribution from Aggregated Mobile Phone Data

2020 ◽  
Vol 10 (18) ◽  
pp. 6580 ◽  
Author(s):  
Alket Cecaj ◽  
Marco Lippi ◽  
Marco Mamei ◽  
Franco Zambonelli
Keyword(s):  
Deep Learning ◽  
Mobile Phone ◽  
Statistical Methods ◽  
Urban Areas ◽  
Domain Knowledge ◽  
Mobile Phone Data ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Crowd Density

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.

scholarly journals Smart Camera Aware Crowd Counting via Multiple Task Fractional Stride Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1346 ◽  
Author(s):  
Minglei Tong ◽  
Lyuyuan Fan ◽  
Hao Nan ◽  
Yan Zhao
Keyword(s):  
Deep Learning ◽  
Receptive Fields ◽  
Density Level ◽  
Smart Camera ◽  
Crowd Counting ◽  
Crowd Density ◽  
Effective Performance ◽  
Density Map ◽  
Deep Learning Model ◽  
Multiple Task

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.

scholarly journals Physical characteristics and spillage detection Using multi-feature fusion

2021 ◽  
Vol 2083 (2) ◽  
pp. 022041
Author(s):  
Caiyu Liu ◽  
Zuofeng Zhou ◽  
Qingquan Wu
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Complex Data ◽  
Learning Approaches ◽  
Road Maintenance ◽  
Data Set ◽  
First Time

Abstract As an important part of road maintenance, the detection of road sprinkles has attracted extensive attention from scholars. However, after years of research, there are still some problems in the detection of road sprinkles. First of all, the detection accuracy of traditional detection algorithm is deficient. Second, deep learning approaches have great limitations for there are various kinds of sprinkles which makes it difficult to build a data set. In view of the above problems, this paper proposes a road sprinkling detection method based on multi-feature fusion. The characteristics of color, gradient, luminance and neighborhood information were considered in our method. Compared with other traditional methods, our method has higher detection accuracy. In addition, compared with deep learning-based methods, our approach doesn’t involve creating a complex data set and reduces costs. The main contributions of this paper are as follows: I. For the first time, the density clustering algorithm is combined with the detection of sprinkles, which provides a new idea for this field. II. The use of multi-feature fusion improves the accuracy and robustness of the traditional method which makes the algorithm usable in many real-world scenarios.

scholarly journals Estimation of Crowd Density from UAV Images based on Deep Learning

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.

scholarly journals Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3478
Author(s):  
Zijian Wang ◽  
Yimin Wu ◽  
Lichao Yang ◽  
Arjun Thirunavukarasu ◽  
Colin Evison ◽  
...  
Keyword(s):  
Deep Learning ◽  
Personal Protective Equipment ◽  
Superior Performance ◽  
Protective Equipment ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
The Novel ◽  
Construction Site ◽  
Construction Sites ◽  
Comparison Result

The existing deep learning-based Personal Protective Equipment (PPE) detectors can only detect limited types of PPE and their performance needs to be improved, particularly for their deployment on real construction sites. This paper introduces an approach to train and evaluate eight deep learning detectors, for real application purposes, based on You Only Look Once (YOLO) architectures for six classes, including helmets with four colours, person, and vest. Meanwhile, a dedicated high-quality dataset, CHV, consisting of 1330 images, is constructed by considering real construction site background, different gestures, varied angles and distances, and multi PPE classes. The comparison result among the eight models shows that YOLO v5x has the best mAP (86.55%), and YOLO v5s has the fastest speed (52 FPS) on GPU. The detection accuracy of helmet classes on blurred faces decreases by 7%, while there is no effect on other person and vest classes. And the proposed detectors trained on the CHV dataset have a superior performance compared to other deep learning approaches on the same datasets. The novel multiclass CHV dataset is open for public use.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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