Social Distance Detector Using Computer Vision and Deep Learning to Combat COVID-19

The study's goal is to create a detector that detects and analyses whether pedestrians or individuals in public gatherings are maintaining social distancing. Drone-shot videos, live webcam feeds, and photographs are all kinds of input for the detector. With no human intervention, Dynamic Detection through live stream provides safety and simplifies monitoring of social distance. The webcam input can be integrated with an external webcam or a drone's camera. Furthermore, the YOLOv4 algorithm is used for the data set for the initial phase ofobject detection, identifying various items in each frame. The recognized objects are narrowed down to humans, and the Euclidian distance between one data point and every other data point is determined The Euclidian distance determines if they are maintaining the minimal distance between them or not by depicting them with a colored border box. Euclidian distance assists in detecting if they are keeping the minimal distance between them or not, as shown by a coloredboundary box, red for unsafe and green for safe, with an indication reflecting the number of people in danger. © 2022 IEEE.

A Deep learning based approach for Social Distance Monitoring

The COVID-19 pandemic has hit the world at large claiming large number of lives till date leaving us with no solution except maintaining social distancing or washing hands regularly, wearing masks and staying at homes. Social distancing is one of the key aspects to prevent spreading of this virus. It means more of maintaining suitable distance between each other. Artificial intelligence has been used widely for a large number of purposes and as such is one of the key tools used here for implementing this project. The proposed system identifies people who are not suitable distance apart by using object detection and calculating the Euclidian distance between two people. This system would be beneficial to the authorities for alerting people if the situation is serious. © 2021 IEEE.

Measurement of micro-harmonic vibration frequency from the modulated self-mixed interferometric signal using dynamic time warping method

The vibration frequency is one of the key factors that contains vital information about the subjects/machines and offers major analytical support. The contactless measurement of vibration frequency is the crucial requirement of many industrial, scientific, and biomedical applications like predictive maintenance, non-destructive testing, and reverse engineering, chest vibration, etc. The paper presents a self-mixed optical feedback interferometry (SM-OFI) sensor to measure the vibration frequency of the micro-harmonic vibrating surface. The method employs a Dynamic Time Warping algorithm (DTW) to compute the Euclidian distance between the locally generated reference signal and the SM interferometric signal obtained from a vibrating target. The method is tested experimentally on a customized SM-OFI emitting a wavelength of 650 nm under weak feedback conditions. The proposed method was able to measure the unknown frequency with 98% accuracy in all sets of experiments. The method also exhibits an R-squared value of 0.99 with a relative error of less than unity. The comprehensive analysis of the experimental results concludes that the proposed method provides an accurate and precise vibration frequency measurement scheme for the low bandwidth range. This low bandwidth range measurement promises a non-contact measurement in industries and biomedical applications during the COVID-19 scenario.