A Study of Hand Motion/Posture Recognition in Two-Camera Views

• Published in: Advances in Visual Computing pp. 314 - 323
• Main Authors: Wang, Jingya, Payandeh, Shahram
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: Camera View; Gesture Recognition; Hand Gesture; Hand Gesture Recognition; Support Vector Machine
• ISBN: 9783319278629; 3319278622
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-27863-6_29

This paper presents a vision-based approach for hand gesture recognition which combines both trajectory recognition and hand posture recognition. With two calibrated cameras, the 3D hand motion trajectory can be reconstructed. The reconstructed trajectory is then modeled by dynamic movement primitives (DMP) and a support vector machine (SVM) is trained to recognize five classes of gestures trajectories. Scale-invariant feature transform (SIFT) is used to extract features on segmented hand postures taken from both camera views. Based on various hand appearances captured by the two cameras, the proposed hand posture recognition method has shown a very good success rate. A gesture vector is proposed to combine the recognition result from both trajectory and hand postures. For our experimental set-up, it was shown that it is possible to accomplish a good overall accuracy for gesture recognition.