New research on People Counting and Human Detection

People counting methods and human detection methods can be considered in determining the number of people in a room. Lately, computer vision in artificial intelligence (AI) is gaining an increasingly important place by interpreting infrared images according to certain characteristics, namely groups of connected pixels in an image that share some common property. This paper starts from three existing systems, two counting the number of people using two PIR sensors, and two ultrasonic sensors and a human detection method, using the Grid-EYE camera. As the Grid-EYE camera resolution is only 8 x 8 pixels, it is not accurate enough and a better solution is being sought. The Lepton infrared camera is chosen, with a resolution of 80 x 60 pixels. Measurements are made in parallel with the two cameras and the results are compared and analyzed. Finally, conclusions are drawn, and applications are exemplified in which it is useful to determine the number of people in a room.

I. Introduction

In recent years, the demand for applications that determine the number of people is growing. Especially for those who do human detection using (AI) and computer vision. AI is the intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans and animals. It includes machine learning and computer vision. Computer vision is a form of AI in which computers can “see” the world, analyze visual data, and then make decisions about it or gain an understanding of the environment and the situation. Counting the number of people can have multiple uses, integration with room reservation applications, estimating people in a crowded area (mall, stadium, theater, and others), determining the parameters that must be set HVAC systems to achieve human thermal comfort in buildings. People counting is a crucial and challenging problem in visual surveillance. An accurate and real-time estimation of people in a shopping mall can provide valuable information for managers. Automatic monitoring of the number of people in public areas is also important for safety control and urban planning [1].