Crowd Density Analysis Research Articles

CRIME PREVENTION AND WORK MONITORING IN CROWD USING CCTV NETWORK

In an era where surveillance technology is becoming increasingly sophisticated, leveraging Closed-Circuit Television (CCTV) networks for crime prevention and crowd monitoring has gained significant attention. This research aims to explore the potential of CCTV systems in deterring criminal activities and ensuring public safety in crowded environments. By integrating advanced video analytics and machine learning algorithms, the CCTV network can detect suspicious behavior patterns, identify potential threats, and alert law enforcement in real-time. Moreover, by employing work monitoring features, such as crowd density analysis and traffic flow optimization, the system can enhance operational efficiency and mitigate safety risks in various public spaces, including transportation hubs, shopping malls, and urban centers. Ultimately, this study seeks to contribute to the development of proactive security measures and intelligent surveillance solutions for safer communities. Keywords— Artificial Intelligence, CCTV Network, Crime Prevention, Crowd Monitoring, Surveillance.

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Cloud‐fog assisted human tracing and disaster evacuation framework

AbstractMany kinds of natural hazards and man‐made calamities have occurred in recent years, each of which has left a huge number of victims and has been challenging to deal with. The solution is an effective evacuation planning system, which is gaining the interest of governments, academic institutions, and industries all across the world. This study focuses on quick and organized evacuation from densely populated areas during disasters to decrease damage successfully. This research proposes a fog‐cloud‐assisted effective disaster evacuation framework. The data accumulation layer uses IoT, mobile networks, and GPS technologies to collect environmental and location‐based data to track the inhabitants. The fog layer is utilized for (i) disaster event detection and (ii) human tracing and crowd density analysis. A cloud layer makes it easier for an evacuation algorithm to produce an evacuation map that will point evacuees to the exit while computing a quick and safe path utilizing environmental and inhabitant information gathered from the fog. The findings reveal that the performance of Model A surpasses the split delivery vehicle routing problem model with the minimum demand of vehicles and with improvement of 38.1% average. Also, A* takes less time (18.3 s) than other state‐of‐the‐art techniques to find the best optimal route for evacuation. Implementation and performance analysis demonstrate the efficiency and effectiveness of the proposed framework.

Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities

Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.

Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage.

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex scene make the analysis of group behavior in video face great challenges. Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition in the video. A crowd detection and location method based on density map regression-guided classification was designed. Finally, a crowd behavior analysis method based on density grade division was designed to complete crowd density analysis and video group behavior detection. In addition, this paper also proposes to extract spatio-temporal features of crowd posture and density by using the double-flow spatio-temporal map network model, so as to effectively capture the differentiated movement information among different groups. Experimental results on public datasets show that the proposed method has high accuracy and can effectively predict group behavior.

CROWD MONITORING USING MACHINE LEARNING

Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, public safety, crowd abnormalities, visual surveillance and urban planning. The purpose of crowd density analysis is to calculate the concentration of the crowd in the videos of observers. The job of detecting a face in the crowd is complicated due to the variability present in human faces including color, pose, expression, position, orientation, and illumination. This paper proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.

Towards crowd density-aware video surveillance applications

Crowd density analysis is a crucial component in visual surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame. The proposed approach consists of generating automatic crowd density maps using local features as an observation of a probabilistic density function. It also involves a feature tracking step which excludes feature points belonging to the background. This process is favorable for the later density estimation as the influence of features irrelevant to the underlying crowd density is removed. Since the proposed crowd density conveys rich information about the local distributions of persons in the scene, we employ it as a side information to complement other tasks related to video surveillance in crowded scenes. First, since conventional detection and tracking methods are hard to be scalable to crowds, we use the proposed crowd density to enhance detection and tracking in videos of high density crowds. Second, we employ the local density together with regular motion patterns as crowd attributes for high level applications such as crowd change detection and event recognition. Third, we investigate the concept of crowd context-aware privacy protection by adjusting the obfuscation level according to the crowd density. In the experimental results, our proposed approach for crowd density estimation is evaluated on videos from different datasets, and the results demonstrate the effectiveness of feature tracks for crowd measurements. Moreover, the employment of crowd density in other applications demonstrate good performances for detection, tracking, behavior analysis, and privacy preservation.

Video-based crowd density estimation and prediction system for wide-area surveillance

Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.

Crowd Density Analysis with Marked Point Processes [Applications Corner

This article presents a Bayesian approach that estimates the count and location of individuals in a video frame. Crowds are modeled by a marked point process (MPP) that couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body size, shape, and orientation. Given a noisy, binary mask image where pixels are labeled foreground or background, the approach seeks a configuration of cutout shapes that simultaneously covers as many foreground pixels and as few background pixels as possible.