Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

Fig. 4: Steps for detect social distance For camera setup, It is shot from a fixed angle as a video frame. Further, the video frame was viewed as a forward view and converted to a two-dimensional view for a more accurate assessment of the distance measurement. This is the main workflow of the model we use to determine social distance. 1. Consider an image/video 2. Divided into small framed 3. Pass an image to SSD-Mobilenet model 4. Extract the features of an image 5. Identify the objects 6. Used euclidean matrix to get each distance of identified boundong boxes. This algorithm calculates distances between people and draw different colors of bounding boxes with fulfilling above steps. Used SSD-MobileNet model for object detection. For better detection for bad visibility areas used CLAHE prepossessing method. c) Human Behaviour Risk Analysis This proposed Human Tracking and Profiling for Risk Management another main part is Human behaviour recognition part. Briefly in this part, Estimate the human actions and then recognize what are the actions using previously estimated actions. In this scenario, mainly there are two main parts in the human action recognition. i. Human action estimation In this part there are also two parts, Which is, single person post estimation and multi person action estimation. Multi-person action estimation is more difficult than single person action estimation, Because, there are more than one object should be locked in the each frame. According to this proposed system we had to use multi person human action estimation method. There are lot of multi human action estimation method. for a example, Open Pose, Alpha Pose , Deep Cut and Mask RCNN. from them, Open Pose, and Deep sort algorithm methods are used to develop this function, Because, it gives more accuracy than other methods, and there are more capable facility to get real time human actions. And another advantage is, Open Pose follows Bottom-Up approach. In the bottom-Up approach, first initially detect the human joints and the connect each joint for each related person. Deep Sort algorithm is mainly used for track multi people. ii. Recognize human Behaviours This is the second part of this function. In this function recognize the what are the human behaviours using previously estimated human actions. To do that, we used a machine learning model that we created using more than 4100 image data as the data set. d) Face Mask Risk Analysis Facemask risk was monitored in real time using a deep learning approach for detecting face masks. This section identifies the type of facemask and calculates the risk by comparing it to the recommended risk values. Two distinct YOLO (V3) object detection models are used to determine whether a face mask is present or absent and to classify the type of face mask. YOLO is an ingenious convolutional neural network (CNN) for real-time object detection. The algorithm applies a single neural network to the entire image, then divides it into regions and predicts their bounding boxes and probabilities. Here the feature maps are obtained by 81,79 and 91 convolutional neural network layers in three detections. In this detection is accomplished by applying detection kernels to feature maps of three distinct sizes located in three distinct locations throughout the network. Due to the difficulty of obtaining a sufficiently large dataset for training the two models, custom datasets were used. A dataset of 6000 images was used to classify four types of facemasks: Surgical, KN95, Homemade, and Bare. Additionally, to deter- mine whether or not to use facemasks, we used a dataset of 4000 images. Where transfer learning was used to train YOLO (V3) models using custom data. Then, using Python, author created an algorithm based on the risk value assigned by the (NCIRD), Division of Viral Diseases. If the area is dangerous, the head of location is notified via SMS. Twilio’s Python library assists in creating a new instance of the Message resource by allowing you to specify the message’s To, From, and Body parameters. Fig. 5: Method of face-mask detection IV. R esults and D iscussion a) Density Risk Analysis Yolov3 network was first trained with a single class dataset of 4000 people images. So the dataset © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 12 ( )D Human Tracking and Profiling for Risk Management Year 2022