I-Corps: Determining occupant load and location through machine vision with on-device image processing

I-Corps：通过机器视觉和设备上的图像处理确定乘员负载和位置

• 批准号: 2054807
• 负责人: Yezhou Yang
• 金额: $ 5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054807&HistoricalAwards=false
• 关键词: Corps; Determining; occupant; load; location

The broader impact/commercial potential of this I-Corps project is the development of smart cameras with on-board processing. The proposed technology will be used as a part of building and smart city management systems. Building heating, ventilation and air conditioning (HVAC) accounts for 13% of all energy usage in the United States and nearly 40% of buildings' energy. Accurate occupancy detection may reduce energy use in HVAC systems by as much as 30%. However, there are concerns regarding occupancy detection when assessing accurate detection and privacy. The proposed technology may solve these concerns by relying on the camera's vision to provide precise information and on-device analysis to ensure no image privacy data is transmitted. The proposed technology also may be used to improve traffic light planning. Pedestrians congregating at an intersection may cause safety issues for vehicles and people. The proposed technology allows for the counting of people, cars, and bikes and the integration of this information. Additional analysis may be performed by providing count data and if pedestrian counts are not changing, there may be a need to modify traffic patterns or alert first responders.This I-Corps project is based on the development of embedded devices to run object-detection algorithms. Object detection using deep neural networks (DNNs) involves a large amount of computation, which impedes its implementation on resource/energy-limited, user-end devices. The reason for the success of DNNs is due to having knowledge over different domains of observed environments. However, only a limited knowledge of the observed environment at inference time is required, which may be learned using a shallow neural network (SHNN). The TKD (Temporal Knowledge Distillation) is a system-level design that is proposed to improve the energy consumption of object detection on the user-end device. An SHNN is deployed on the user-end device to detect objects in the observing environment. Also, a knowledge transfer mechanism is implemented to update the SHNN model using the DNN knowledge when there is a change in the object domain. Experiments demonstrate that the user-end device's energy consumption and the inference time can be improved by 78% and 71% compared with running the deep model on the user-end device.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The broader impact/commercial potential of this I-Corps project is the development of smart cameras with on-board processing. The proposed technology will be used as a part of building and smart city management systems.建筑物的供暖，通风和空调（HVAC）占美国所有能源使用量的13％，占建筑物能源的近40％。 Accurate occupancy detection may reduce energy use in HVAC systems by as much as 30%. However, there are concerns regarding occupancy detection when assessing accurate detection and privacy.提出的技术可以通过依靠相机的愿景来提供精确的信息和设备分析来解决这些问题，以确保没有传输图像隐私数据。 The proposed technology also may be used to improve traffic light planning. Pedestrians congregating at an intersection may cause safety issues for vehicles and people. The proposed technology allows for the counting of people, cars, and bikes and the integration of this information.可以通过提供计数数据来执行其他分析，如果行人计数不变，则可能需要修改流量模式或警报第一响应者。此I-Corps项目基于开发嵌入式设备以运行对象检测算法。 使用深神经网络（DNN）的对象检测涉及大量计算，这阻碍了其对资源/能量限制的用户端设备的实现。 The reason for the success of DNNs is due to having knowledge over different domains of observed environments.但是，只需要在推理时间观察到的环境有限的知识，可以使用浅神经网络（SHNN）学习。 TKD（时间知识蒸馏）是一种系统级设计，旨在改善用户端设备上对象检测的能量消耗。 An SHNN is deployed on the user-end device to detect objects in the observing environment.此外，在对象域发生更改时，还可以实现知识传输机制，以使用DNN知识更新SHNN模型。实验表明，与运行用户端设备上的深层模型相比，用户端设备的能耗和推理时间可以提高78％和71％。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。