Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

Abstract

The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.

TIMo—A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera

“…A head template is matched to the resulting image in order to find the person’s location. For the mechanical safety applications, the approach of Zhou et al [ 35 ] detects a person in RGB frames and then finds the associated distance to the person to prevent entering the dangerous area. Wetzel et al [ 36 ] use a fusion of information from multiple depth cameras for person detection from different viewpoints.…”

A Stable and Durable Triboelectric Nanogenerator for Speed Skating Land Training Monitoring

“…A computer vision system with a parallel camera was designed to track the trajectory of athletes in short-track speed skating competitions [25], but the system is affected by its high cost, high power consumption, and high lighting requirements. A depth camera was proposed for human position detection [26], but this camera cannot achieve the recognition of human motion states, especially muscle and joint movements. Therefore, it is crucial to develop a simple, economical, lightweight, and portable self-powered wearable sensor for speed skating land training monitoring.…”