People Monitoring and Mask Detection Using Real-Time Video Analyzing

EasyChair Preprint 4997

Abstract

People Counting and mask detection based on video is an important field in a Computer Vision. There is a growing interest in video-based solutions for people monitoring and counting in business and security applications using Computer Vision technology. It has been effectively used in many Artificial Intelligence fields. Compared to classic sensor based solutions the video based ones allow for more flexible functionalities, improved performance with lower costs. The people counter requires more powerful processing since it deals with real-time video, so the proposed method converts a color image into binary in order to minimize data of image. Reducing development time is important term in Software Engineering to build a system.

People counting method based on head detection and tracking to evaluate the number of people who moves under an over-head camera and check whether people are wearing a mask or not. There are four main features in this proposed system: People counting, Mask detection, Alarm alert and Scan ID. Based on head tracking, this method uses the crossing-line judgment to determine whether the candidate head object will be counted or not to be counted.

The two main challenges addressed in this system are: tough estimation of the background scene and the number of real persons in merge-split scenarios. A technique for masked face detection using three different steps of estimating eye line detection, facial part detection and eye detection is used in this system. On exceeding the count of people or in case mask is not worn then alarm gets alerted.

Keyphrases: MobileNet, SSD Convolutional Neural Network, dataset

@booklet{EasyChair:4997,
  author    = {Yogesh Gowari and Ritik Gaikwad and Aniket Gurav and Vinit Raut},
  title     = {People Monitoring and Mask Detection Using Real-Time Video Analyzing},
  howpublished = {EasyChair Preprint 4997},
  year      = {EasyChair, 2021}}