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

物理空间被划分，以形成为人类服务于各种目的的基础设施、建筑和设施。例如，在公共设施的设计中，创造走廊或通道是为了促进人员从一端到另一端的流动。同样，在部署摄像机网络时，放置摄像机是为了捕捉人类活动并获取可识别的信息。然而，在上面的例子中，在设计相机放置的基础设施时，分别考虑了非常有限的观察到的人类行为信息。可持续性、成本、美观等因素往往占据主导地位。这个i-Corps团队认为，在设计设施或在摄像机网络中设计摄像头的位置时，理解人类行为与其他因素具有同等重要性。该团队已经开发出一种方法，可以预测任何基础设施中预期的人类运动行为，这反过来又被用来通知摄像头的放置，以形成视频摄像头网络。该解决方案优化了捕获可分析的人力数据的能力，同时确保了当前部署中使用的传统智慧，包括监控固定资产、进出方式和开放空间的能力。建议的解决方案为摄像机放置提供了经济高效的设计，以形成一个可生成可操作数据的摄像机网络，从而最大化已部署系统的价值和投资回报(ROI)。建议的技术核心包括人体运动行为的方法和模型。它包括基于已知几何体的基础设施内人体运动行为的计算模型，以及模拟已知几何体中的人体运动的计算模型。基本方法利用心理学和社会学领域的人类行为概念，并使用计算机科学优化技术来预测人类行为。该团队相信，建议的方法可以满足或缓解当前与有效监控相关的行业需求，并降低成本以实现可操作的数据。在实现上述目标的过程中，该团队将确定现有的视频监控系统提供商、设计师和集成商；将与客户进行面谈并验证他们的需求，并确定可以在最低限度可行的产品(MVP)中开发的功能特性。该团队还将确定开发原型MVP所需的开发资源和设施，并在可能的情况下，努力将开发的方法整合到遵循敏捷开发方法的原型产品中。

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.