I-Corps: Human Behavioral Models for Camera Placement and Video Analytics

University Of Houston, Houston TX

Investigators

Abstract

Physical spaces are compartmentalized to form infrastructures, buildings, and facilities that serve various purposes for humans. For example, in the design of a public facility, corridors or passageways are created to facilitate the flow of humans from one end to another. Similarly, in deploying a video camera network, the cameras are placed to capture human activities and obtain identifiable information. However, a very limited amount of observed human behavioral information is taken into consideration while designing the infrastructure of the placement of cameras, respectively, in the above examples. Factors like sustainability, costs, aesthetics, etc. tend to take precedence. This I-Corps team believes that human behavior understanding holds equal importance as other factors in designing a facility or in designing the placement of cameras in a camera network. The team has developed an approach that predicts expected human motion behavior in any infrastructure, which in turn is leveraged to inform the placement of cameras to form a video camera network. This solution optimizes the ability to capture analyzable human data while ensuring traditional wisdom used in current deployments that include the ability to monitor fixed assets, entry/exit ways, and open spaces. the proposed solution delivers a cost-effective design for camera placements to form a video camera network that generates actionable data and hence maximizes the value and return-on-investment (ROI) of the deployed system. The proposed technology core comprises of methods and models for human motion behavior. It includes a computational model of human motion behavior within an infrastructure based on its known geometry, and a computational model to simulate human motion in a known geometry. The underlying methods leverage concepts of human behavior from the fields of psychology and sociology, and employs computer science optimization techniques to predict human behavior. This team believes that the proposed methods can fulfill or alleviate current industry needs related to effective surveillance at reduced costs to realize actionable data. In realizing the above, the team will identify existing video surveillance system providers, designers, and integrators; will perform customer interviews and validate their needs and identify functional features that could be developed in a minimal viable product (MVP). The team will also identify the development resources and facilities necessary to develop the prototype MVP and, if possible, work towards integrating developed methods into a prototype product following agile development methods.