Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system

Pedestrian Flow Simulation Validation and Verification Techniques

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analyzing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge. The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.Comment: 19 pages, 10 figure

Modelling of crowd behaviour during indoor/outdoor evacuation

In pedestrian dynamics, there are forces that usually define the trajectories between the moving individuals. These can be more easily understood in corresponding video scenes where the crowd movement arrives also from the application of such forces like Newton laws as friction and action-reaction. Nevertheless, the complexity of the individuals' movement insinuates that, between them, there are also more complex or "invisible" unknown forces as the ones described by psychological terms that apply and dictate the crowd movement. In this thesis, we want to indicate the existence of those forces by modeling the movement of a human as an oscillation described by rhythms and/or frequencies perceived from its environment. Moreover, we intend to describe with the help of appropriate computational tools and detection and tracking software, the movement of a crowd as a network of rhythms and show that the correlation among the individuals' rhythm can lead to phenomena of synchronization and self-organization of the moving crowd

A graphical simulator for modeling complex crowd behaviors

Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts - video recordings - from the real-world

Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009

In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate. In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density.The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of the local densities in the Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented.Comment: 20 pages, 17 figures, correction of some reference

Time-continuous microscopic pedestrian models: an overview

We give an overview of time-continuous pedestrian models with a focus on data-driven modelling. Starting from pioneer, reactive force-based models we move forward to modern, active pedestrian models with sophisticated collision-avoidance and anticipation techniques through optimisation problems. The overview focuses on the mathematical aspects of the models and their different components. We include methods used for data-based calibration of model parameters, hybrid approaches incorporating neural networks, and purely data-based models fitted by deep learning. Some development perspectives of modelling paradigms we expect to grow in the coming years are outlined in the conclusion.Comment: 26 pages; chapter accepted for publication in Crowd Dynamics (vol. 4

Explicit Energy-Minimal Short-Term Path Planning for Collision Avoidance in Crowd Simulation

In traditional crowd simulation methods, global path planning (GPP) and local collision avoidance (LCA) were mostly used to advance pedestrians toward their own goals without colliding. However, we found that using those methods in bidirectional flows can force a pedestrian to get stuck among the incoming people, walk through the congestion, or even unintentionally occupy in a dense area, although more comfortable passageway exists. These odd behaviors are usually produced and simply noticeable in bidirectional case. This paper aims at reducing these artifacts to achieve more behavioral fidelity, by adding the explicit metabolic-energy-minimal short-term path planning (MEM) in between GPP and LCA. For energy analysis, the optimal control theory with the objective energy function from the study of biomechanics was employed, which finally leads to the useful optimal walking characteristics for the pedestrians. The simulation results show that the pedestrians with MEM can adapt their moving to avoid the congestion, resulting in more promising lane changing and overtaking behaviors. Even though MEM was mainly developed to deal with the artifacts in bidirectional flows, it can be extended with a little modification and can produce significant behavioral improvement for multi-directional case as shown in the last part of the paper