Soraia Raupp Musse

A Model of Human Crowd Behavior : Group Inter-Relationship and Collision Detection Analysis

This paper presents a model of crowd behavior to simulate the motion of a generic population in a specific environment. The individual parameters are created by a distributed random behavioral model which is determined by few parameters. This paper explores an approach based on the relationship between the autonomous virtual humans of a crowd and the emergent behavior originated from it. We have used some concepts from sociology to represent some specific behaviors and represent the visual output. We applied our model in two applications: a graphic called sociogram that visualizes our population during the simulation, and a simple visit to a museum. In addition, we discuss some aspects about human crowd collision.

Crowd Analysis Using Computer Vision Techniques

This article presents a survey on crowd analysis using computer vision techniques, covering different aspects such as people tracking, crowd density estimation, event detection, validation, and simulation. It also reports how related the areas of computer vision and computer graphics should be to deal with current challenges in crowd analysis.

Modeling individual behaviors in crowd simulation

This paper presents a model for studying the impact of individual agent characteristics in emergent groups, based on the evacuation efficiency as a result of local interactions. We used the physically based model of crowd simulation proposed by Helbing et al. (2000) and generalized it in order to deal with different individualities for agent and group behaviors. In addition, we present a framework to visualize the virtual agents and discuss the obtained results. A variety of simulations with different parameter sets shows significant impact on the evacuation scenario.

Background Subtraction and Shadow Detection in Grayscale Video Sequences

Tracking moving objects in video sequence is an important problem in computer vision, with applications several fields, such as video surveillance and target tracking. Most techniques reported in the literature use background subtraction techniques to obtain foreground objects, and apply shadow detection algorithms exploring spectral information of the images to retrieve only valid moving objects. In this paper, we propose a small improvement to an existing background model, and incorporate a novel technique for shadow detection in grayscale video sequences. The proposed algorithm works well for both indoor and outdoor sequences, and does not require the use of color cameras.

Crowd modelling in collaborative virtual environments

1. ABSTRACT This paper presents a crowd modelling method in Collaborative Virtual Environment (CVE) which aims to create a sense of group presence to provide a more realistic virtual world. An adaptive display is also presented as a key element to optimise the needed information to keep an acceptable frame rate during crowd visualisation. This system has been integrated in the several CVE platforms which will be presented at the end of this paper.

A paradigm for controlling virtual humans in urban environment simulations

This paper presents a new architecture for simulating virtual humans in complex urban environments. The approach is based on the integration of six modules. Four key modules are used in order to manage environmental data, simulate human crowds, control interactions between virtual humans and objects, and generate tasks based on a rule-based behavioral model. The communication between these modules is made through a client/server system. Finally, all low-level virtual human actions are delegated to a single motion and behavioral control module. Our architecture combines various human and object simulation aspects, based on the coherent extraction and classification of information froma virtual city database. This architecture is discussed in this paper, together with a detailed case study example.

Using computer vision to simulate the motion of virtual agents

In this paper, we propose a new model to simulate the movement of virtual humans based on trajectories captured automatically from filmed video sequences. These trajectories are grouped into similar classes using an unsupervised clustering algorithm, and an extrapolated velocity field is generated for each class. A physically‐based simulator is then used to animate virtual humans, aiming to reproduce the trajectories fed to the algorithm and at the same time avoiding collisions with other agents. The proposed approach provides an automatic way to reproduce the motion of real people in a virtual environment, allowing the user to change the number of simulated agents while keeping the same goals observed in the filmed video. Copyright

Understanding people motion in video sequences using Voronoi diagrams: Detecting and classifying groups

This work describes a model for understanding people motion in video sequences using Voronoi diagrams, focusing on group detection and classification. We use the position of each individual as a site for the Voronoi diagram at each frame, and determine the temporal evolution of some sociological and psychological parameters, such as distance to neighbors and personal spaces. These parameters are used to compute individual characteristics (such as perceived personal space and comfort levels), that are analyzed to detect the formation of groups and their classification as voluntary or involuntary. Experimental results based on videos obtained from real life as well as from a crowd simulator were analyzed and discussed.

A Background Subtraction Model Adapted to Illumination Changes

This paper presents a new adaptive background model for grayscale video sequences, that includes shadows and highlight detection. In the training period, statistics are computed for each image pixel to obtain the initial background model and an estimate of the image global noise, even in the presence of several moving objects. Each new frame is then compared to this background model, and spatio-temporal features are used to obtain foreground pixels. Local statistics are then used to detect shadows and highlights, and pixels that are detected as either shadow or highlight for a certain number of frames are adapted to become part of the background. Experimental results indicate that the proposed algorithm can effectively detect shadows and highlights, adapting the background with respect to illumination changes.

Event Detection Using Trajectory Clustering and 4-D Histograms

In this paper, we propose a framework for event detection based on trajectory clustering and 4-D histograms. In the training period, captured trajectories are grouped into coherent clusters according to global motion flows. Within each cluster, the position and instantaneous velocity of each tracked object are used to build a 4-D motion histogram for the cluster. In the test period, each new trajectory is compared against the 4-D histograms of all clusters, so that its coherence with previously tracked objects can be evaluated. Experimental results showed that these criteria can be effectively used to measure the coherence of test trajectories with those in the training stage, allowing a range of events to be detected in surveillance and traffic applications.