有人在吗？智能嵌入式低分辨率全向视频传感器，用于测量房间占用率
Anyone here? Smart embedded low-resolution omnidirectional video sensor to measure room occupancy
摘要：
在本文中，我们提出了一种具有独特特性的房间占用感测解决方案：（i）它基于全向视觉摄像头，可以在广角范围内捕获丰富的场景信息，从而能够计算房间中的人数，甚至他们的位置。(ii)虽然它使用摄像头输入，但由于其极低的图像分辨率，不会出现隐私问题，使人无法识别。(iii)神经网络推理完全在嵌入传感器的低成本处理平台上运行，进一步降低了隐私风险。(iv)由于我们提出的自训练方案，需要有限的手动数据注释。这样的智能房间占用率传感器可以用于例如会议室和灵活办公桌。事实上，通过鼓励弹性办公桌，所需的办公空间可以大大减少。但在某些情况下，预订的灵活服务台在预订系统中没有更新的情况下仍然无人使用。类似的问题也发生在会议室，这些会议室往往没有被占用。通过优化入住率，可以大幅降低成本。因此，在本文中，我们开发了这样的系统，它确定了目前在办公室灵活的办公桌和会议室的人数。使用安装在天花板上的全向摄像头，结合人员探测器，该公司可以根据测量的入住率智能更新预订系统。在这种自训练全向人检测算法的优化和嵌入式实现之后，在这项工作中，我们提出了一种新的方法，该方法结合了空间和时间图像数据，提高了我们的系统在极低分辨率图像上的性能。
英文摘要：
In this paper, we present a room occupancy sensing solution with unique properties: (i) It is based on an omnidirectional vision camera, capturing rich scene info over a wide angle, enabling to count the number of people in a room and even their position. (ii) Although it uses a camera-input, no privacy issues arise because its extremely low image resolution, rendering people unrecognisable. (iii) The neural network inference is running entirely on a low-cost processing platform embedded in the sensor, reducing the privacy risk even further. (iv) Limited manual data annotation is needed, because of the self-training scheme we propose. Such a smart room occupancy rate sensor can be used in e.g. meeting rooms and flex-desks. Indeed, by encouraging flex-desking, the required office space can be reduced significantly. In some cases, however, a flex-desk that has been reserved remains unoccupied without an update in the reservation system. A similar problem occurs with meeting rooms, which are often under-occupied. By optimising the occupancy rate a huge reduction in costs can be achieved. Therefore, in this paper, we develop such system which determines the number of people present in office flex-desks and meeting rooms. Using an omnidirectional camera mounted in the ceiling, combined with a person detector, the company can intelligently update the reservation system based on the measured occupancy. Next to the optimisation and embedded implementation of such a self-training omnidirectional people detection algorithm, in this work we propose a novel approach that combines spatial and temporal image data, improving performance of our system on extreme low-resolution images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
