Isolated sign language recognition using improved dense trajectories

Abstract

Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action recognition. Fisher Vectors (FV) are used to represent sign samples in the proposed method. Seven different combinations of features were compared using a test set of 200 signs, using a Support Vector Machine (SVM) classifier. The best combination yielded 80; 43% recognition performance when Histogram of Optical Flow (HOF) and Motion Boundary Histogram (MBH) components were used together.