LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Agent Behaviour Simulator (ABS):a platform for urban behaviour development

Computer Graphics have become important for many applicationsand the quality of the produced images have greatly improved. Oneof the interesting remaining problems is the representation of densedynamic environments such as populated cities. Although recentlywe saw some successfulwork on the rendering such environments,the real?time simulation of virtual cities populated by thousands ofintelligent animated agents is still very challenging.In this paperwe describe a platformthat aims to accelerate the developmentof agent behaviours. The platform makes it easy to enterlocal rules and callbacks which govern the individual behaviours.It automatically performs the routine tasks such as collision detectionallowing the user to concentrate on defining the more involvedtasks. The platform is based on a 2D-grid with a four-layered structure.The two first layers are used to compute the collision detectionagainst the environment and other agents and the last two are usedfor more complex behaviours.A set of visualisation tools is incorporated that allows the testingof the real?time simulation. The choices made for the visualisationallow the user to better understand the way agents move inside theworld and how they take decisions, so that the user can evaluate ifit simulates the expected behaviour.Experimentation with the system has shown that behaviours inenvironments with thousands of agents can be developed and visualisedin effortlessly

Assessing the perceived realism of agent crowd behaviour within virtual urban environments using psychophysics

Inhabited virtual environments feature in a growing number of graphical applications. Simulated crowds are employed for different purposes; ranging from evaluation of evacuation procedures to driving interactable elements in video games. For many applications, it is important that the displayed crowd behaviour is perceptually plausible to the intended viewers. Crowd behaviour is inherently in flux, often depending upon many different variables such as location, situation and crowd composition. Researchers have, for a long time, attempted to understand and reason about crowd behaviour, going back as far as famous psychologists such as Gustave Le Bon and Sigmund Freud who applied theories of mob psychology with varying results. Since then, various other methods have been tried, from articial intelligence to simple heuristics, for crowd simulation. Even though the research into methods for simulating crowds has a long history, evaluating such simulations has received less attention and, as this thesis will show, increased complexity and high-delity recreation of recorded behaviours does not guarantee improvement in the plausibility for a human observer. Actual crowd data is not always perceived more real than simulation, making it dicult to identify gold standards, or a ground truth. This thesis presents new work on the use of psychophysics for perceptual evaluation of crowd simulation in order to develop methods and metrics for tailoring crowd behaviour for target applications. Psychophysics itself is branch of psychology dedicated to studying the relationship between a given stimuli and how it is perceived. A three-stage methodology of analysis, synthesis and perception is employed in which crowd data is gathered from the analysis of real instances of crowd behaviour and then used to synthesise behavioural features for simulation before being perceptually evaluated using psychophysics. Perceptual thresholds are calculated based on the psychometric function and key congurations are identied that appear the most perceptually plausible to human viewers. The method is shown to be useful for the initial application and it is expected that it will be applicable to a wide range of simulation problems in which human perception and acceptance is the ultimate measure of success