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Featured Application: The proposed app can help quickly calculate the number of people, avoid crowd gathering, and cause the risk of group infections for COVID-19. Calculating the number of people is often necessary and repeated in real life. As the number of people increases, the calculation is time-consuming. Efficiently calculating the number of people is helpful to human life. In this article, we propose a valuable app to quickly calculate the number of people in a photo by a convolutional neural network (CNN). Initially, suspected face areas are segmented into micro-blocks. The segmented blocks are then confirmed through the CNN by rejecting the segmented micro-blocks without the human face to ensure the detection accuracy of the face area. The experimental results reveal that the proposed app can efficiently calculate the number of people. The world is now seriously threatened by the COVID-19 epidemic. The proposed app can help quickly calculate the number of people, avoid crowd gathering, and cause the risk of group infections. [ABSTRACT FROM AUTHOR]

Calculating the number of people is often necessary and repeated in real life. As the number of people increases, the calculation is time-consuming. Efficiently calculating the number of people is helpful to human life. In this article, we propose a valuable app to quickly calculate the number of people in a photo by a convolutional neural network (CNN). Initially, suspected face areas are segmented into micro-blocks. The segmented blocks are then confirmed through the CNN by rejecting the segmented micro-blocks without the human face to ensure the detection accuracy of the face area. The experimental results reveal that the proposed app can efficiently calculate the number of people. The world is now seriously threatened by the COVID-19 epidemic. The proposed app can help quickly calculate the number of people, avoid crowd gathering, and cause the risk of group infections.

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C.

This paper presents the design principles underlying the video nodes of long-lifetime wireless networks. The hardware and firmware architectures of the system are described in detail, along with the system-power-consumption model. A prototype is introduced to validate the proposed approach. The system mounts a Flash-based field-programmable gate array and a high-dynamic-range complementary metal–oxide–semiconductor custom vision sensor. Accurate power measurements show that the overall consumption is 4.2 mW at 3.3 V in the worst case, thus achieving an improvement of two orders of magnitude with respect to video nodes for similar applications recently proposed in the literature. Powered with a 2200-mAh 3.3-V battery, the system will exhibit a typical lifetime of about three months. [ABSTRACT FROM AUTHOR]

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people's direction, which allows monitoring users' occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C. [ABSTRACT FROM AUTHOR]