A Method for Detecting Non-Mask Wearers Based on Regression Analysis

A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.

A novel particle tracking algorithm using polar coordinate system similarity

A new algorithm using polar coordinate system similarity （PCSS） for tracking particle in particle tracking velocimetry （PTV） is proposed. The essence of the algorithm is to consider simultaneously the changes of the distance and angle of surrounding particles relative to the object particle. Monte Carlo simulations of a solid body rotational flow and a parallel shearing flow are used to investigate flows measurable by PCSS and the influences of experimental parameters on the implementation of the new algorithm. The results indicate that the PCSS algorithm can be applied to flows subjected to strong rotation and is not sensitive to experimental parameters in comparison with the conventional binary image cross-correlation （BICC） algorithm. Finally, PCSS is applied to images of a real experiment.