Crowd analysis

Crowd analysis involves the interpretation of data gained studying the natural movement of groups or objects. Masses of bodies, particularly humans, are the subjects of these crowd tracking analyses that include how a particular crowd moves and when a movement pattern changes. The data is implemented in order to predict future crowd movement, crowd density, and potential events such as an evacuation route. Applications of crowd analysis can range from video game crowd simulation to security and surveillance. Crowd analysis involves the interpretation of data gained studying the natural movement of groups or objects. Masses of bodies, particularly humans, are the subjects of these crowd tracking analyses that include how a particular crowd moves and when a movement pattern changes. The data is implemented in order to predict future crowd movement, crowd density, and potential events such as an evacuation route. Applications of crowd analysis can range from video game crowd simulation to security and surveillance. Due to population growth, crowd analysis has become a major interest in social and technical disciplines. Crowd analysis is being used to develop crowd management strategies in public events as well as public space design, visual surveillance and virtual environments to make areas more convenient in order to prevent crowd induced disasters. Certain crowds cannot be analyzed as easily as others. The psychology of a crowd impacts how it will be broken up and studied. Crowds can be casual, such as a group of pedestrian walking down the road, or causal, like people participating in a marathon or protest. They can be as active an erratic as a mob or as passive as an audience. While the main crowd is the subject of the bulk of the analysis, anomalies must be taken into account, like someone opposing the flow of traffic or a biker travelling through a group of walkers. Hence, the purpose of a group of individuals determines the interpretation of the data obtained. Significant research has been done to understand the way crowds moves in order to predict where areas of conflicts may occur. This research is done by analyzing data from crowds, and then proceeding to create models of similar situations using software. Many models that simulate crowd behavior exists, with some stating 'macroscopic models like network-based models or fluid-dynamics models as well as microscopic models like e.g. the Social Force Model or Cellular Automata.' Crowd density refers to the number of objects within a unit area, such as people per square meter. Density is important to determine the maximum occupancy of a room or building to address safety concerns. Analyzing areas which become more densely packed than others is essential for designing buildings and evacuation routes. Addressing such concerns involve the management and optimization of the crowd and its predict movement patterns. Crowd flow involves the speed with which objects in a crowd are able to move in a space. There is a critical capacity where flow begins to decrease as the crowd's density increases. The Yerkes-Dodson law explains how performance is impacted by the amount of stress on an individual. The stress is caused by external factors such as an object coming at the individual, a time constraint for the individual to perform a task, or the number of agents crowding an individual. In regards to computer animation, simulated individuals (referred to as agents) are often written to portray realistic crowd-like behavior. They follow algorithm based on stress, navigation fields, and surrounding agents in order to manipulate behavior. The study of producing intelligent agents to follow lifelike behavior falls under the field of artificial intelligence.