Ioannis Karamouzas

A Predictive Collision Avoidance Model for Pedestrian Simulation

We present a new local method for collision avoidance that is based on collision prediction. In our model, each pedestrian predicts possible future collisions with other pedestrians and then makes an efficient move to avoid them. Experiments show that the new approach leads to considerably shorter and less curved paths, ensuring smooth avoidance behaviour and visually compelling simulations. The method reproduces emergent behaviour like lane formation that have been observed in real crowds. The technique is easy to implement and is fast, allowing the simulation in real time of crowds of thousands of pedestrians.

Universal Power Law Governing Pedestrian Interactions

Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power-law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds, and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.

Simulating and Evaluating the Local Behavior of Small Pedestrian Groups

Recent advancements in local methods have significantly improved the collision avoidance behavior of virtual characters. However, existing methods fail to take into account that in real life pedestrians tend to walk in small groups, consisting mainly of pairs or triples of individuals. We present a novel approach to simulate the walking behavior of such small groups. Our model describes how group members interact with each other, with other groups and individuals. We highlight the potential of our method through a wide range of test-case scenarios. We evaluate the results from our simulations using a number of quantitative quality metrics, and also provide visual and numerical comparisons with video footages of real crowds.

Indicative routes for path planning and crowd simulation

An important challenge in virtual environment applications is to steer virtual characters through complex and dynamic worlds. The characters should be able to plan their paths and move toward their desired locations, avoiding at the same time collisions with the environment and with other moving entities. In this paper we propose a general method for realistic path planning, the Indicative Route Method (irm). In the irm, a so-called indicative route determines a global route for the character, whereas a corridor around this route is used to handle a broad range of other path planning issues, such as avoiding characters and computing smooth paths. As we will show, our method can be used for real-time navigation of many moving characters in complicated environments. It is fast, flexible and generates believable paths.

Simulating the local behaviour of small pedestrian groups

Recent advancements in local methods have significantly improved the collision avoidance behaviour of virtual characters. However, existing methods fail to take into account that in real-life pedestrians tend to walk in small groups, consisting mainly of pairs or triples of individuals. We present a novel approach to simulate the walking behaviour of such small groups. Our model describes how group members interact with each other, with other groups and individuals. We highlight the potential of our method through a wide range of test-case scenarios. A number of metrics are also proposed to quantitatively evaluate the quality of our proposed model.

A velocity-based approach for simulating human collision avoidance

We present a velocity-based model for realistic collision avoidance among virtual characters. Our approach is elaborated from experimental data and is based on the simple hypothesis that an individual tries to resolve collisions long in advance by slightly adapting its motion.

A Data-Driven Framework for Visual Crowd Analysis

We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.

Exploiting Motion Capture to Enhance Avoidance Behaviour in Games

Realistic simulation of interacting virtual characters is essential in computer games, training and simulation applications. The problem is very challenging since people are accustomed to real-world situations and thus, they can easily detect inconsistencies and artifacts in the simulations. Over the past twenty years several models have been proposed for simulating individuals, groups and crowds of characters. However, little effort has been made to actually understand how humans solve interactions and avoid inter-collisions in real-life. In this paper, we exploit motion capture data to gain more insights into human-human interactions. We propose four measures to describe the collision-avoidance behavior. Based on these measures, we extract simple rules that can be applied on top of existing agent and force based approaches, increasing the realism of the resulting simulations.

Using the Corridor Map Method for Path Planning for a Large Number of Characters

A central problem in games is planning high-quality paths for characters avoiding obstacles in the environment. Current games require a path planner that is fast (to ensure real-time interaction) and flexible (to avoid local hazards). In addition, a path needs to be natural, meaning that the path is smooth, short, keeps some clearance to obstacles, avoids other characters, etcetera . Game worlds are normally populated with a large number of characters. In this paper we show how the recently introduced Corridor Map Method can be extended and used to efficiently compute smooth motions for these characters. We will consider crowds in which the characters wander around, characters have goals, and characters behave as a coherent group. The approach is very fast. Even in environments with 5000 characters it uses only 40% of the processing power of a single core of a cpu . Also the resulting paths are indeed natural.

Simulating Human Collision Avoidance Using a Velocity-Based Approach

We present a velocity-based model for realistic collision avoidance among virtual characters. Our approach is elaborated from existing experimental data and is based on the simple hypothesis that an individual tries to resolve collisions long in advance by slightly adapting its motion. We have evaluated our model by testing it against a wide range of challenging scenarios. In all of our simulations, the characters exhibit smooth and visually convincing motions, avoiding all collisions with minimal effort. The method reproduces emergent behaviour, like lane formation, that have been observed in real crowds. It is relatively easy to implement and and is fast, allowing the simulation of crowds of thousands of characters in real time.