Defining Crowd Movement as Parabola and Classifying These Definitions

Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management; however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd density observation with the particle filter algorithm, the present method estimates and forecasts the large crowd flow using agent-based simulations that incorporate observation data. Numerical experiments, including a realistic evacuation scenario with 5000 individuals, demonstrated that the present method could successfully provide reasonable crowd flow forecasting for different crowd scenarios, even with limited information on crowd movements. These results support the feasibility of real-time crowd flow forecasting and subsequent crowd management, even for large but microscopic crowd problems.

IntroductionIn the past decade, India has witnessed many lapses in crowd safety during mass gatherings. The high casualty rate in stampedes during traditional mass gatherings has prompted the study of these events. Wide variations exist in casualty rates for similar events, and key issues in healthcare services in these special situations were addressed in the Indian context.MethodsFrom 2001–2010, Mass gathering data were collected from news items reported in the archives of newspapers, “The Times of India”, “The Hindu” and “The Indian Express”. The keywords used were: “stampede”, “mass gathering”, “mass-gathering events”, “mass-gathering incidents”, “crowd”, and “crowd management”. The study included triggers for the incident and the number of casualties (dead and injured) in each incident.ResultsIn 27 separate mass gatherings in India, there were 936 dead and 540 injured casualties. The unique characteristics of mass gatherings in India included a predominance of old and vulnerable people in traditional mass gatherings, in contrast to the young and middle-aged groups who gather for music and sporting events elsewhere. Further, alcohol/substance abuse, brawls, and violent behavior were absent at traditional Indian mass gatherings. Non-traditional mass gatherings accounted for a lesser number of incidents in India, and were located in movie theatres and railway stations.ConclusionsIn a populous country like India, traditional mass gatherings predominate, and ensuring the health, safety, and security of the public at such events will require an understanding of crowd behavior, critical crowd densities, and crowd capacities in the Indian context. However, planning for mass gatherings can be developed using the existing body of knowledge of mass-casualty preparedness, food safety, and health promotion.

Abstract: WiFiTally is a cutting-edge technology designed for monitoring and counting crowds in public spaces using Wi-Fi signals. Imagine a system that can track and analyze the movement of people in areas like shopping malls, airports, or event venues without invading privacy. WiFiTally achieves this by tapping into the Wi-Fi signals emitted by smartphones and other connected devices carried by individuals. In simple terms, WiFiTally observes the patterns of Wi-Fi signals within a designated area, providing insights into crowd density and movement. Instead of relying on traditional methods like cameras, this technology prioritizes privacy by using existing Wi-Fi signals that people willingly emit from their devices. The process involves collecting anonymous data from Wi-Fi signals and transforming it into valuable information about crowd size and flow. It doesn't identify individuals; instead, it focuses on overall trends, helping businesses and event organizers make informed decisions about crowd management. WiFiTally offers a non-intrusive and privacy-friendly solution for understanding and optimizing crowd dynamics. This abstract aims to introduce the concept of WiFiTally crowd monitoring and counting, emphasizing its user-friendly approach and respect for personal privacy in public spaces.

In this paper, we present an approach to the driver head motion tracking problem using Bayesian learning and inter-frame optical flow clustering. Two cameras (one operating in visual band and the other in the infra-red range) are mounted on vehicle dashboard to determine the driver head motion by examining the temporal variation of video-histogram differences (VHD). Progresively increasing VHD indicates that the driver head moves from its steady position. An optical flow map is generated as a measure that would lead the tracker in the direction of head motion in synthetically generated volumetric view geometry involving a sequence of motion frames. Experiments show that the proposed system can track the driver head and identify the motion as left or right in two consecutive video frames parallel to the image plane of the camera or forward and backward direction from one frame to next. This simplifies the problem of tackling head movement with arbitrary motion in space via considering two consecutive frames at a time and marking the motion from the current frame to the next in sequence. Since the head position is marked in each frame with its optical flow track, the arbitrary motion is viewed as a classification problem of belonging to left-right, right-left, backward-forward, and forward-backward in the inter-frame duration via the Bayesian learning process of the Gaussian density.

The success of deep learning-based techniques in solving various computer vision problems motivated the researchers to apply deep learning to predict the optical flow of a video in the next frame. However, the problem of predicting the motion of an object in the next few frames remains an unsolved and less explored problem. Given a sequence of frames, predicting the motion in the next few frames of the video becomes difficult in cases where the displacement of optical flow vector across frames is large. Traditional CNNs often fail to learn the dynamics of the objects across frames in case of large displacements of objects in consecutive frames. In this paper, we present an efficient CNN based on the concept of feature pyramid for extracting the spatial features from a few consecutive frames. The spatial features extracted from consecutive frames by a modified PWC-Net architecture are fed into a bidirectional LSTM for obtaining the temporal features. The proposed spatiotemporal feature pyramid is able to capture the abrupt motion of the moving objects in video, especially when displacement of the object is large across the consecutive frames. Further, the proposed spatiotemporal pyramidal feature can effectively predict the optical flow in next few frames, instead of predicting only the next frame. The proposed method of predicting optical flow outperforms the state of the art when applied on challenging datasets such as “MPI Sintel Final Pass,” “Monkaa” and “Flying Chairs” where abrupt and large displacement of the moving objects in consecutive frames is the main challenge.

Real time object tracking is the process of locating moving objects over time using the camera in video sequences in real time. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires location and shape or features of objects in the video frames. Every tracking algorithm needs to detect moving object. So object detection is the preceding step of object tracking in computer vision applications. After that, detected object can be extracted by the feature of moving object to track that moving object into video scene. It is challenging task in image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to scene and real time processing requirements. Object tracking has a variety of uses, some of which are: surveillance and security, traffic monitoring, video communication, robot vision and animation.

A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings

Crowd management is a significant topic especially for countries that support gathering events frequently. The Kingdom of Saudi Arabia hosts and manages one of the world class annual religious gatherings known as “pilgrimage”. Several challenges are raised for managing and controlling such mass gathering event. In this paper we propose a comprehensive framework for event processes modelling and management. The framework consists of four main stages starts with acquiring temporal data and ends by modelling different processes of the event. The main contribution of this work is to demonstrate how process mining techniques can be used innovatively to model the movement flow of crowd. Synthetic data is used to show a proof-of-concept of the proposed framework and the applicability of using it in modelling and monitoring real crowd movement scenarios.

This study presented a cost- optimised smart wireless surveillance system based on Internet of Things (IoT) technology for real-time monitoring and motion detection using the ESP32-CAM module. Conventional surveillance systems are constrained by wired infrastructure, high installation costs, and limited remote accessibility, which restrict their deployment in dynamic and resourceconstrained environments. To address these limitations, the proposed system integrates an ESP32-CAM module with a passive infrared (PIR) motion sensor and built-in Wi-Fi communication to enable automated monitoring and real-time data transmission. The system adopts a layered IoT architecture comprising sensing, processing, network, and application layers. The sensing layer captures visual and motion data, while the processing layer, implemented within the ESP32-CAM, performs data handling and event triggering. The network layer facilitates wireless communication for live video streaming and alert delivery, and the application layer provides user access through web-based and mobile interfaces. The system was developed using the Arduino Integrated Development Environment and implemented as a functional prototype. Experimental evaluation demonstrated that the system achieved real-time video streaming with a response time of 1.3 seconds, motion detection accuracy of 91%, and low power consumption of 0.85 W. These results indicate reliable performance and suitability for deployment in residential and small-scale commercial environments. The indings conirm that IoTbased surveillance systems provide a scalable, energy-eficient, and cost-effective alternative to traditional security solutions. Future work will focus on integrating artiicial intelligence for intelligent detection, implementing robust security mechanisms, and incorporating cloud-based analytics to enhance system performance and scalability.

Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

A huge mass gathering is a common scenario on various occasion when millions of people congest comparatively in a small place. During this time crowd management and controlling is become a very difficult and crucial task. Sometimes there were accidents such as stampede in which thousands of casualties during such convention. This research deals with crowd congestion to provide crowd safety and stampede avoidance. For this research purpose, Hajj is considered as the case study where we are proposed an E-wrist belt which is the integration of various sensor and Wemos D1 based on Internet of Things (IoT). Generally, stampede occurs in any large assembly mainly because of fall down of attendance, temperature and humidity. The machine learning approach is used for evaluating stampede probability by assessing temperature, humidity and fall of attendance. The objective of this research is to improve the existing crowd management controlling system in hajj by using E-wrist belt.

An automated system for crowd behaviour analysis has gained significance in the context of surveillance and public management. Detecting the changes in the crowd behaviour demarcates one activity or event from another. Thus, change detection is a fundamental step that enables the subsequent characterisation of the activities and analysis of the transition from one state to another. Proposed work deals with high density crowd. Global motion is an important cue for studying the behaviour of such crowd. In this work, crowd motion is modelled using beta distribution. Change in the distribution parameter is an indicator for change in crowd motion pattern. Proposed methodology has been tested with number of synthetic and natural video sequences and the performance is satisfactory.

This paper presents a new algorithm for object motion detection and trajectory tracking. This method was developed as part of a machine vision system for human fertility analysis. Fertility analysis is based on the amount of spermatozoa in semen samples and their type of movement. Two approaches were tested to detect the movement of the spermatozoa, image subtraction, and optical flow. Image subtraction is a simple and fast method but it has some complications to detect individual motion when large amounts of objects are presented. The optical flow method is able to detect motion but it turns to be computationally time expensive. It does not generate a specific trajectory of each spermatozoon, and it does not detect static spermatozoa. The algorithm developed detects object motion through an orthogonal search of blocks in consecutive frames. Matching of two blocks in consecutive frames is defined by square differences. A dynamic control array is used to store the trajectory of each spermatozoon, and to deal with all the different situations in the trajectories like, new spermatozoa entering in a frame, spermatozoa leaving the frame, and spermatozoa collision. The algorithm developed turns out to be faster than the optical flow algorithm and solves the problem of the image subtraction method. It also detects static spermatozoa, and generates a motion vector for each spermatozoon that describes their trajectory.

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios.

The world population continues to grow. The size of gatherings at various venues under different circumstances is tremendously increasing. Any mass assembly has potential risk of the lethal crowd disaster. The various algorithms in computer vision techniques are being developed as a part of proactive system for crowd management. This paper presents novel approach to identify abnormal crowd behavior. The algorithm employedoptical flow method to estimate displacement vectors of moving crowd and computation of crowd motion energy. The crowd motion energy was further modified by crowd motion intensity (CMI). The peaks in the CMI characteristics were the indicators of abnormal activity and were detected more accurately by applying threshold. The algorithm has been tested on standard UMN dataset with an average accuracy of 91.66 percentages. The accuracy has been improved with application of threshold. Adaptive threshold may aid to further improve the performance of algorithm.