Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance

Due to the spread of COVID-19, people wearing face masks became a regular occurrence worldwide. Moreover, there are nations where covering one's face is done for religious or cultural reasons, or even wear face masks for convenience. However, current face detection and tracking systems are hindered by face masks as the full facial features are no longer visible and therefore became less effective. In this paper, it is proposed to improve current face detection and long-term tracking technology by extracting the facial features of the top regions of the face, taking into account the eye, eyebrow, and forehead. The methodology contains two models, the face detector and the long-term object tracker. The face detection model uses a joint dataset from ISL-UFMD and MaskedFace-Net. The dataset is used to train a Keras sequential model. The object detection model uses pre-trained YOLOv4 weights and DeepSORT to identify people and uses the tracking-by-detection method to perform long-term tracking throughout the surveillance video. The final face detection model results show a testing accuracy of 93.33% and a loss of 26.92%, which are up to par and comparable with other state-of-the-art models. © 2022 IEEE.

Implementation of Access Control System based on Face Prediction and Face Tracking

Problems caused by COVID-19, which have been continuing for two years from 2020, are constantly being raised. In particular, time is wasted in measuring body temperature and verifying occupants every time they enter a building. In the existing access control method, manpower is placed at each entrance to check whether or not a mask is worn or not. Accordingly, there is a problem that there is a risk of wasting manpower and infection, and recognition takes a long time. Therefore, it is necessary to manage building access more effectively by providing a non-face-to-face environment suitable for the intact era. Therefore, in this paper, we designed and developed a system to control access by examining the condition of whether or not a mask is worn when entering a building. For this purpose, a face was detected using the Single Shot Multibox Detector (SSD) algorithm, which has a better recognition rate than the existing face detection algorithm. In addition, the detected face image was input to the deep learning model to examine whether it was worn. Finally, a system was implemented that allows the administrator to manage the building with one application by transmitting the result of whether or not the person is wearing a mask to the back-end server. © 2022, Success Culture Press. All rights reserved.

A Six-degree-of-freedom Face Tracking Method for Non-contact Physiological Detection Robot

Due to the COVID-19 epidemic, there has been a high demand for non-contact diagnostic equipment that can reduce exposure and cross-infection. A non-contact medical detection robot is a type of diagnostic equipment and medical service robot with a wide application prospect. However, few non-contact medical detection robots have been designed to collect patients' physiological parameters when they are in inconvenient situations, such as bedridden, during clinical usage. A six-degree-of-freedom (six-DOF) face tracking method based on a six-DOF robot is proposed in this paper. In the proposed system, a face detector equipped with a camera attached to the robot's wrist is used to obtain the real-time face depth and attitude information. The optimal target attitude of the camera is calculated according to the constraints in the base coordinate system. A closed-loop controller is designed to control the robot to reach the target position and posture smoothly. The experiment with a six-DOF robot has verified that the proposed system can achieve the real-time tracking of human faces by a camera. The proposed method can also be used in many other scenarios where six-DOF face tracking is required by robots.

Development of an autonomously navigating robot capable of conversing and scanning body temperature to help screen for COVID-19

Throughout the COVID-19 pandemic, the most common symptom displayed by patients has been a fever, leading to the use of temperature scanning as a preemptive measure to detect for potential carriers of the virus. Human employees with handheld thermometers have been used to fulfill this task, however this puts them at risk as they cannot be physically distanced, and the sequential nature of this method leads to great inconveniences and inefficiency. The proposed solution is an autonomously navigating robot capable of conversing and scanning people's temperature to detect fevers and help screen for COVID-19. To satisfy this objective, the robot must be able to navigate autonomously, detect and track people, get their temperature reading, and converse with them if it exceeds 38°C. An autonomously navigating mobile robot is used with a manipulator controlled by a face tracking algorithm, and an end effector consisting of a thermal camera, smartphone, and chatbot. In addition, technical challenges encountered, and their engineering solutions will be presented, and recommendations will be made for enhancements that could be incorporated when approaching commercialization. © 2021 IEEE.

Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

A surveillance system is still the most exciting and practical security system to prevent crime effectively. Surveillance systems run on edge devices such as the low-cost Raspberry mobile camera with the Internet of Things (IoT). The primary purpose of this system is to recognize the identity of the face caught by the camera. However, it raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. Moreover, the challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. The surveillance system integrated three modules: Multi-Task Cascaded Convolutional Network (MTCNN) face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker. We train new face mask data for face recognition and tracking. This system utilizes the Raspberry Pi camera and processes the frame on the cloud as a mobile sensor approach. The proposed method was successfully implemented and got competitive detection, recognition, and tracking results under an unconstrained surveillance camera. © 2021. All Rights Reserved.