Rujula Singh R

Sahana Srinivasan, Rujula Singh R, Ruchita R Biradar et al.

In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, Density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along with the labelled video dataset to compensate for the lack of dataset in the community and is used for evaluation of the system. The system performance is evaluated in terms of accuracy, F1 score as well as the prediction time, which has to be low for practical applicability. The system performs with an accuracy of 91.2% and F1 score of 90.79% on the labelled video dataset and has an average prediction time of 7.12 seconds for 78 frames of a video.

Nikhil Nayak, Rujula Singh R, Rameshwar Garg et al.

In today's day and age, a mobile phone has become a basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary to accurately predict the user traffic in cellular networks, so as to improve the performance in terms of resource allocation and utilisation. Since traffic learning and prediction is a classical and appealing field, which still yields many meaningful results, there has been an increasing interest in leveraging Machine Learning tools to analyse the total traffic served in a given region, to optimise the operation of the network. With the help of this project, we seek to exploit the traffic history by using it to predict the nature and occurrence of future traffic. Furthermore, we classify the traffic into particular application types, to increase our understanding of the nature of the traffic. By leveraging the power of machine learning and identifying its usefulness in the field of cellular networks we try to achieve three main objectives - classification of the application generating the traffic, prediction of packet arrival intensity and burst occurrence. The design of the prediction and classification system is done using Long Short Term Memory (LSTM) model. The LSTM predictor developed in this experiment would return the number of uplink packets and also estimate the probability of burst occurrence in the specified future time interval. For the purpose of classification, the regression layer in our LSTM prediction model is replaced by a softmax classifier which is used to classify the application generating the cellular traffic into one of the four applications including surfing, video calling, voice calling, and video streaming.