A robust deep belief network-based approach for recognizing dynamic hand gestures
Ni Liu, Muhammad Ali Abdul Aziz · 2016
The task of dynamic hand gesture recognition includes several challenges arising due to variations in hand appearances, scale of the hand and the spatio-temporal variability of the gestures. To overcome these difficulties, in this work, we present a robust approach to detect and track a hand, and to recognize the hand trajectory based on DBNs (Deep Belief Networks) in conjunction with the SVM+HOG (Support Vector Machines + Histograms of Oriented Gradients) framework and the Mean Shift approach. Our method relies on merging the advantages of accurate feature-based hand detection, fast local searching-based hand tracking, and smart deep learning-based hand trajectory recognition. Our approach entails three different phases, i.e. hand detection, hand tracking and hand trajectory recognition. The first two phases work in collaboration, with the SVM+HOG framework utilized for hand detection and the Mean Shift algorithm used for tracking the hand. For the third phase, we design a method based on DBNs. Through ample experiments, we prove that our proposed hand tracker outperforms several other state-of-the-art trackers and our hand gesture recognition approach shows excellent results on numerous challenging video sequences.