Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

Automated crowd behaviour analysis and monitoring is a challenging task due to the unpredictable nature of the crowd within a particular scene and across different scenes. The prior knowledge of the type of scene under consideration is a crucial mid-level information, which could be utilized to develop robust crowd behaviour analysis systems. In this paper, we propose an approach to automatically detect the type of a crowded scene based on the global motion patterns of the objects within the scene. Three different types of scenes whose global motion pattern characteristics vary from uniform to non-uniform are considered in this work, namely structured, semi-structured, and unstructured scenes, respectively. To capture the global motion pattern characteristics of an input crowd scene, we first extract the motion information in the form of trajectories using a key-point tracker and then compute the average angular orientation feature of each trajectory. This paper utilizes these angular features to introduce a novel feature vector, termed as Histogram of Angular Deviations (HAD), which depicts the distribution of the pair-wise angular deviation values for each trajectory vector. Since angular deviation information is resistant to changes in scene perspectives, we consider it as a key feature for distinguishing the scene types. To evaluate the effectiveness of the proposed HAD-based feature vector in classifying the crowded scenes, we build a crowd scene classification model by training the classical machine learning algorithms on the publicly available Collective Motion Database. The experimental results demonstrate the superior crowd classification performance of the proposed approach as compared to the existing methods. In addition to this, we propose a technique based on quantizing the angular deviation values to reduce the feature dimension and subsequently introduce a novel crowd scene structuredness index to quantify the structuredness of an input crowded scene based on its HAD.

The Behaviour of Home Advantage during the COVID-19 Pandemic in European Rink Hockey Leagues

The primary purpose of the present study was to compare the home advantage (HA) and the home team performance in the most relevant European rink hockey leagues (Spanish, Portuguese and Italian), considering the presence or absence of spectators in the competition venues due to the effect of COVID-19 restrictions. The sample was composed of 1665 rink hockey matches (654 from the Spanish league, 497 from the Portuguese league, and 514 from the Italian league) played between the 2018–2019 and 2020–2021 seasons. The HA and match variables comparisons were established using several negative binomial regression models. Results showed that the effect of HA did not disappear despite playing without spectators but decreased from 63.99% to 57.41% (p = 0.002). Moreover, the comparison of the match variables showed that playing with spectators benefited local teams’ performance, especially in the Portuguese and Italian leagues. Playing with spectators favoured local team performance in rink hockey matches, which is more evident in some analysed leagues. However, as HA does not disappear entirely without spectators, it is necessary to study other relevant performance factors that are not directly or indirectly attributable to crowd behaviour in rink hockey performance analyses.

Identification of crowd behaviour patterns using stability analysis

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.

State-space models reveal bursty movement behaviour of dance event visitors

Pedestrian movements during large crowded events naturally consist of different modes of movement behaviour. Despite its importance for understanding crowd dynamics, intermittent movement behaviour is an aspect missing in the existing crowd behaviour literature. Here we analyse movement data generated from nearly 600 Wi-Fi sensors during large entertainment events in the Johan Cruijff ArenA football stadium in Amsterdam. We use the state-space modeling framework to investigate intermittent motion patterns. Movement models from the field of movement ecology are used to analyse individual pedestrian movement. Joint estimation of multiple movement tracks allows us to investigate statistical properties of measured movement metrics. We show that behavioural switching is not independent of external events, and the probability of being in one of the behavioural states changes over time. In addition, we show that the distribution of waiting times deviates from the exponential and is best fit by a heavy-tailed distribution. The heavy-tailed waiting times are indicative of bursty movement dynamics, which are here for the first time shown to characterise pedestrian movements in dense crowds. Bursty crowd behaviour has important implications for various diffusion-related processes, such as the spreading of infectious diseases.

DISNEY2GO solution to overcrowded theme parks: tech based crowd mitigation tool

The purpose of this project is to investigate the effectiveness of how a mobile app integrating augmented reality and GPS technology can influence crowd behaviour in the themed entertainment industry. In partnership with Dr. Asgary, Associate Professor of Disaster & Emergency Management at York University and a member ADERSIM, the study was conducted on the AnyLogic Simulation system to measure how Disney characters can act as a crowd mitigation tool to influence crowd movements throughout the Magic Kingdom. Using data to represent park entrance rates, attraction duration, and wait times, the study was able to capture the level of influence Disney characters had on park guest’s movements throughout their visit. This simulation reveals that Disney characters have the ability to influence crowd behaviour with a probability rate of approximately 30%. This data supports the view that the proposed mobile app will act as an effective crowd mitigation tool and can strategically influence crowd migration throughout the Magic Kingdom.

DISNEY2GO solution to overcrowded theme parks: tech based crowd mitigation tool

The purpose of this project is to investigate the effectiveness of how a mobile app integrating augmented reality and GPS technology can influence crowd behaviour in the themed entertainment industry. In partnership with Dr. Asgary, Associate Professor of Disaster & Emergency Management at York University and a member ADERSIM, the study was conducted on the AnyLogic Simulation system to measure how Disney characters can act as a crowd mitigation tool to influence crowd movements throughout the Magic Kingdom. Using data to represent park entrance rates, attraction duration, and wait times, the study was able to capture the level of influence Disney characters had on park guest’s movements throughout their visit. This simulation reveals that Disney characters have the ability to influence crowd behaviour with a probability rate of approximately 30%. This data supports the view that the proposed mobile app will act as an effective crowd mitigation tool and can strategically influence crowd migration throughout the Magic Kingdom.