Crowd Monitoring Research Articles

A Grey Wolf Optimization Based Algorithm for Optimum Camera Placement

Camera placement is very important for surveillance applications. Proposed work presents a new method of optimum placement of visual sensors for maximum coverage of the predefined surveillance space. The surveillance space is modeled as priority areas (PAs), obstacles and feasible locations for placement of cameras. We are using PTZ (pan, tilt, zoom) cameras, which not only reduces occlusion due to randomly moving objects in the PA but also increases the covered area compared to pin hole cameras. The proposed approach will be useful for crowd monitoring in a big surveillance space holding multiple events and having multiple entrances. The problem of optimum camera placement for maximum coverage considering both static and randomly moving obstacles is mapped as a Grey Wolf Optimization (GWO) problem. The proposed algorithm is computationally lighter and converges faster as compared to Genetic Algorithm (GA) based camera placement and Particle Swarm Optimization (PSO) based camera placement algorithm. The concept is validated using simulation as well as the experimental results.

Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data.

Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences.

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

The rising need of crowd monitoring in public spaces, especially for safety purposes, pushes the research community to propose and experiment novel methods of crowd density estimation. This paper focuses on device-free RF sensing, which does not require the monitored people to carry any electronic device. In particular, the paper proposes and assesses the performance of a crowd density estimation system based on the analysis of the variations of Channel State Information (CSI) computed from unencrypted synchronization signals transmitted by a eNodeB and reflected/scattered by people located in the monitored area. The proposed method uses features extracted from the list of singular values of the CSI secant set. This approach allows to reduce the impact of CSI variations due to noise or HW instability, improving the sensitivity to CSI variations caused by human presence. The average accuracy achieved by the proposed approach is 84%, which is comparable with the accuracy achieved with WiFi based crowd density estimation systems.

Practical Automated Video Analytics for Crowd Monitoring and Counting

Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

ABSTRACTWith rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.

Analyzing the Crowd Evacuation Pattern of a Large Densely Populated Building

Understanding crowd evacuation behavior is of utmost importance for large buildings in order to achieve efficient crowd monitoring and management. This paper presents the simulation and analysis of crowd evacuation pattern for a large building called Al Masjid An Nabawi, widely known as ‘the Haram,’ in Madinah, Saudi Arabia. Legion Evac software is employed to simulate the crowd evacuation. During simulation, Legion computes various metrics that holistically reflect the crowd evacuation pattern, which captures the crowd evacuation behavior. We analyze the magnitude and temporal variations with respect to the general evacuation patterns (GEP) of the building. The magnitude is analyzed using the t-test, which is a hypothesis testing method. However, the temporal variations are analyzed using cross-correlation analysis. The GEP captures the general crowd evacuation behavior (across all sections) of the building by aggregating the evacuation patterns of each section of the building. The crowd evacuation simulation resulted in an evacuation time of 21 min to evacuate a population of approximately 170,000. The analysis of evacuation patterns shows that the evacuation pattern of different sections of the building differs significantly in magnitude, but has significant temporal similarity with respect to GEP. Finally, insights are derived from the analysis results, which aid in efficient crowd monitoring and management.

Virtual Sensing and Virtual Reality: How New Technologies Can Boost Research on Crowd Dynamics.

The collective behavior of human crowds often exhibits surprisingly regular patterns of movement. These patterns stem from social interactions between pedestrians such as when individuals imitate others, follow their neighbors, avoid collisions with other pedestrians, or push each other. While some of these patterns are beneficial and promote efficient collective motion, others can seriously disrupt the flow, ultimately leading to deadly crowd disasters. Understanding the dynamics of crowd movements can help urban planners manage crowd safety in dense urban areas and develop an understanding of dynamic social systems. However, the study of crowd behavior has been hindered by technical and methodological challenges. Laboratory experiments involving large crowds can be difficult to organize, and quantitative field data collected from surveillance cameras are difficult to evaluate. Nevertheless, crowd research has undergone important developments in the past few years that have led to numerous research opportunities. For example, the development of crowd monitoring based on the virtual signals emitted by pedestrians' smartphones has changed the way researchers collect and analyze live field data. In addition, the use of virtual reality, and multi-user platforms in particular, have paved the way for new types of experiments. In this review, we describe these methodological developments in detail and discuss how these novel technologies can be used to deepen our understanding of crowd behavior.

Monitoring the Number of Pedestrians in an Area: The Applicability of Counting Systems for Density State Estimation

Crowd monitoring systems are more and more used to support crowd management organizations. Currently, counting systems are often used to provide quantitative insights into the pedestrian traffic state, since they are fairly easy to install and the accuracy is reasonably good under normal conditions. However, there are no sensor systems that are 100% accurate. Detection errors might have severe consequences for the density state estimation at large squares. The consequences of these errors for pedestrian state estimation have not yet been determined. This paper studies the impact of one specific type of detection error on the functionality of counting camera systems for density state estimation, namely, a randomly occurring “false negative” detection error. The impact is determined via two tracks, a theoretical track and a simulation track. The latter track studies the distribution of the cumulative number of pedestrians after 24 hours for three stylized cases by means of Monte Carlo simulations. This paper finds that counting camera systems, which have a detection error that is not correlated with the flow rate, provide a reasonably good estimation of the density within an area. At the same time, if the detection error is correlated with the flow rate, counting camera systems should only be used in the situation where symmetric demand patterns are expected.

AN ANALYSIS OF CONTEXT-AWARE DATA MODELS FOR SMART CITIES: TOWARDS FIWARE AND ETSI CIM EMERGING DATA MODEL

Abstract. Smart Cities requires the support of context-aware and enriched semantic descriptions to support a scalable and cross-domain development of smart applications. For example, nowadays general purpose sensors such as crowd monitoring (counting people in an area), environmental information (pollution, air quality, temperature, humidity, noise) etc. can be used in multiple solutions with different objectives. For that reason, a data model that offers advanced capabilities for the description of context is required. This paper presents an overview of the available technologies for this purpose and how it is being addressed by the Open and Agile Smart Cities principles and FIWARE platform through the data models defined by the ETSI ISG Context Information Management (ETSI CIM).

Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

<p>Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.</p>

ANALYSIS OF COMBINED UAV-BASED RGB AND THERMAL REMOTE SENSING DATA: A NEW APPROACH TO CROWD MONITORING

Abstract. Collecting vast amount of data does not solely help to fulfil information needs related to crowd monitoring, it is rather important to collect data that is suitable to meet specific information requirements. In order to address this issue, a prototype is developed to facilitate the combination of UAV-based RGB and thermal remote sensing datasets. In an experimental approach, image sensors were mounted on a remotely piloted aircraft and captured two video datasets over a crowd. A group of volunteers performed diverse movements that depict real world scenarios. The prototype is deriving the movement on the ground and is programmed in MATLAB. This novel detection approach using combined data is afterwards evaluated against detection algorithms that only use a single data source. Our tests show that the combination of RGB and thermal remote sensing data is beneficial for the field of crowd monitoring regarding the detection of crowd movement.

An Energy Efficient Optimal Load Sharing Technique in WSN

Wireless sensor networks have become increasingly popular for environmental and activity monitoring, such as temperature, pollution, parking space, traffic, and crowd monitoring. Mobile users can collect and visualize sensing data by communicating with wireless sensors along their walks using Bluetooth or NFC. They can also share the sensing data on the Internet through 3G or WiFi connectivity. Nevertheless, mobile users may not be able to collect all the data from the sensors due to limited contact times and batteries. In this research a review of different techniques to be used for clustering in WSN.

Wireless vision-based fuzzy controllers for moving object tracking using a quadcopter

During the last few decades, the utilization of unmanned aerial vehicles has grown in military and has seen new civil applications because of their reduced cost and hovering capabilities. This article presents a visual servoing system for detecting and tracking a moving object using an unmanned aerial vehicle. The system consists of two sequential components. The first component addresses the detection of a moving object, using the unmanned aerial vehicle based on color features. The second component addresses the visual servoing control that is designed to guide the unmanned aerial vehicle according to the target position and inclination. Three fuzzy logic modules are implemented in order to control the unmanned aerial vehicle in tracking a moving object. The proposed method is validated on a real flight using an AR.Drone 2.0 quadcopter. The obtained results show that the performance of the proposed method is suitable for object-following tasks in surveillance applications. This research investigates the use of unmanned aerial vehicle technology for crowd monitoring during Hajj and it could also be used for border surveillance to monitor Saudi Arabia’s borders.

An intelligent decision computing paradigm for crowd monitoring in the smart city

The ever-expanding urbanization and the advent of smart cities need better crowd management and security surveillance systems. Advanced systems are required to improve and automate the crowd management system. The aim of the closed circuit television and visual monitoring systems using multiple cameras faces many challenges like illumination variance, occlusion and small spatial–temporal resolution, person in sleep, shadows, dynamic backgrounds, and noises. Therefore, the crowd monitoring, prevention of stampedes and crowd-related emergencies in the smart cities are major challenging problems. In this paper, we propose an intelligent decision computing based paradigm for crowd monitoring in the smart city. In the intelligent computing based framework, the optimization algorithm is applied to compute the feature of crowd motion and measure the correlation between agents based motion model and the crowd data using extended Kalman filtering approach and KL-divergence technique. The proposed framework measures the correlation measure based on extracted novel distinctive feature, and holistic feature of crowd data represent and to classify the crowd motion of individual. Our experimental results demonstrate that the proposed approach yields 96.20% average precision in classifying real-world highly dense crowd scenes.

Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors

Crowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In this paper, we propose to quantify crowd properties by a rich set of visual descriptors. The calculation of these descriptors is realized through a novel spatio-temporal model of the crowd. It consists of modeling time-varying dynamics of the crowd using local feature tracks. It also involves a Delaunay triangulation to approximate neighborhood interactions. In total, the crowd is represented as an evolving graph, where the nodes correspond to the tracklets. From this graph, various mid-level representations are extracted to determine the ongoing crowd behaviors. In particular, the effectiveness of the proposed visual descriptors is demonstrated within three applications: crowd video classification, anomaly detection, and violence detection in crowds. The obtained results on videos from different data sets prove the relevance of these visual descriptors to crowd behavior analysis. In addition, by means of comparisons to other existing methods, we demonstrate that the proposed descriptors outperform the state-of-the-art methods with a significant margin using the most challenging data sets.

Overcrowding detection in indoor events using scalable technologies

The increase in the number of large-scale events held indoors (i.e., conferences and business events) opens new opportunities for crowd monitoring and access controlling as a way to prevent risks and provide further information about the event's development. In addition, the availability of already connectable devices among attendees allows to perform non-intrusive positioning during the event, without the need of specific tracking devices. We present an algorithm for overcrowding detection based on passive Wi-Fi requests capture and a platform for event monitoring that integrates this algorithm. The platform offers access control management, attendees monitoring and the analysis and visualization of the captured information, using a scalable software architecture. In this paper, we evaluate the algorithm in two ways: First, we test its accuracy with data captured in a real event, and then we analyze the scalability of the code in a multi-core Apache Spark-based environment. The experiments show that the algorithm provides accurate results with the captured data, and that the code scales properly.

Estimation of crowd density in surveillance scenes based on deep convolutional neural network

As an effective way for crowd monitoring, control and behavior understanding, crowd density estimation is an important research topic in artificial intelligence applications. In this paper, we propose a new crowd density estimation method by deep convolutional neural network (ConvNet). The contributions are two-folds: first, typical deep networks are imported for crowd density estimation. Second, a new dataset including 31 crowd Subway-carriage scenes with over 160K density annotated images is introduced to better evaluate the accuracy of cross-scene crowd density estimation methods. Experiment results confirm the good performance of our proposed method for real-world application.

Positioning in Wireless Sensor Network for Human Sensing Problem

Abstract: In this paper the possibility of utilization of wireless sensor network in application to human sensing problem is examined. With the emphasis on crowd dynamics monitoring, the most critical issues regarding this application are analyzed. Based on limitations and shortcomings of contemporary crowd monitoring approaches, as well as the specificity of the problem, modified positioning algorithm, combined with noisy data preprocessing technique is proposed. Moreover, physical principles of interaction between pedestrians and sensor network are analyzed. An experiment had been conducted to demonstrate the concept of data collection system aimed to obtain relevant information regarding pedestrian movements.

Development of Crowd Monitoring and Informing System using Fog Computing Model

Development of Crowd Monitoring and Informing System using Fog Computing Model

A Literature Review on Crowd Scene Analysis and Monitoring

A Literature Review on Crowd Scene Analysis and Monitoring