Robust least squares twin support vector machine for human activity recognition

Human activity recognition is an active area of research in Computer Vision. One of the challenges of activity recognition system is the presence of noise between related activity classes along with high training and testing time complexity of the system. In this paper, we address these problems by introducing a Robust Least Squares Twin Support Vector Machine (RLS-TWSVM) algorithm. RLS-TWSVM handles the heteroscedastic noise and outliers present in activity recognition framework. Incremental RLS-TWSVM is proposed to speed up the training phase. Further, we introduce the hierarchical approach with RLS-TWSVM to deal with multi-category activity recognition problem. Computational comparisons of our proposed approach on four well-known activity recognition datasets along with real world machine learning benchmark datasets have been carried out. Experimental results show that our method is not only fast but, yields significantly better generalization performance and is robust in order to handle heteroscedastic noise and outliers. (C) 2016 Elsevier B.V. All rights reserved.