This marks the beginnings of modeling individual behavior in its most elementary form on humanoid agents or virtual humans. The control of human crowds was designated as a hierarchical organization with levels of autonomy amongst agents. Building off of the advanced work of Reynolds, Musse and Thalmann began to study the modeling of real time simulations of these crowds, and their applications to human behavior.
Reynolds states the processes of low-level locomotion to be dependent and reliant on mid-level steering behaviors and higher-level goal states and path finding strategies. Steering behaviors are proven to play a large role in the process of automating agents within a simulation. In 1999, individualistic navigation began its course within the realm of crowd simulation via continued research of Craig Reynolds. Here a relation is drawn between the autonomous behavior of the individual within the crowd and the emergent behavior originating from this. These two present a new model of crowd behavior in order to create a simulation of generic populations. Initial research in the field of crowd simulation began in 1997 with Daniel Thalmann's supervision of Soraia Raupp Musse's PhD thesis. The realistic quality of simulation was engaged with as the individual agents were equipped with synthetic vision and a general view of the environment within which they resided, allowing for a perceptual awareness within their dynamic habitats. The theorization and study set forth by Reynolds was improved and built upon in 1994 by Xiaoyuan Tu, Demetri Terzopoulos and Radek Grzeszczuk. All agents within these simulations were given direct access to the respective positions and velocities of their surrounding agents. He had simulated flocks of birds alongside schools of fish for the purpose of studying group intuition and movement. In 1987, behavioral animation was introduced and developed by Craig Reynolds. Evidently many new findings are continually made and published following these which enhance the scalability, flexibility, applicability, and realism of simulations: Many major advancements have taken place since the beginnings of research within the realm of crowd simulation. There has always been a deep-seated interest in the understanding and gaining control of motional and behavior of crowds of people. 3.5 Leader behavior during evacuation simulations.3.1 Algorithm by Patil and Van Den Berg.
Some more general systems are researched that can support different kinds of agents (like cars and pedestrians), different levels of abstraction (like individual and continuum), agents interacting with smart objects, and more complex physical and social dynamics.
Many crowd steering algorithms have been developed to lead simulated crowds to their goals realistically.
In games and applications intended to replicate real-life human crowd movement, like in evacuation simulations, simulated agents may need to navigate towards a goal, avoid collisions, and exhibit other human-like behavior. For realistic and fast rendering of a crowd for visual media or virtual cinematography, reduction of the complexity of the 3D scene and image-based rendering are used, while variations in appearance help present a realistic population. Ĭrowd simulation may focus on aspects that target different applications. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation. Crowd simulation is the process of simulating the movement (or dynamics) of a large number of entities or characters.