Vision based Human Activity Recognition using Skeleton Data

Abstract

Vision based Human Activity Recognition or simply HAR is a widely researched area that is helpful in understanding human behaviour in images and videos. HAR is an important part of various research problems such as detecting and preventing crimes with the help of automated video surveillance, robot movement without human intervention and to provide telecare for elderly. In this research, an algorithm for activity recognition using 2D pose information extracted from human skeleton is implemented. The approach is based on angles between the joints and displacement of joints between frames. Two publically available datasets are used for training and testing purpose. For activity recognition, five well known techniques of supervised machine learning are implemented separately including K nearest neighbours, SVM, Linear Discriminant, Naïve Bayes and Back propagation neural network. Using these techniques, four action classes Sit, Stand, Fall and Walk, are recognized in videos. Results for all the classifiers are compared to find the best performing technique for the proposed methodology. All classifiers performed well with the best performing classifier achieving an overall accuracy of 98%. The results show that proposed methodology gives compatible accuracy with the state of the art in this field.