Hierarchical Least Square Twin Support VectorMachines Based Framework for Human ActionRecognition

The aim of this paper is presentation of a new humanaction recognition framework. In the proposed framework, localspace-time features extracted by use of Harris detector algorithmand Histogram of Optical Flow (HOF). A new classifier based ontwo non-parallel hyperplanes called Twin Support VectorMachines (TWSVM) is used which is four times faster thanclassical SVM. According to the prior knowledge that two classesof human action recognition (jogging and running) are verysimilar and recognition of these classes are difficult, ahierarchical structure is used for better recognition. We appliedour method to KTH dataset to investigate the performance of theproposed action recognition approach. Our experimental resultshown that our approach improves state-of-the-art results byachieving 98.33%, 96.39% in case of leave-one-out and 10-foldcross validation.

Keywords:

Action Recognition , Twin Support Vector Machines , Histogram of Optical Flow (HOF) , Harris , PCA , KTH dataset ,