Feature Processing and Modeling for 6D Motion Gesture Recognition

• Published in: IEEE transactions on multimedia Vol. 15; no. 3; pp. 561 - 571
• Main Authors: Mingyu Chen, AlRegib, G., Biing-Hwang Juang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2013; Institute of Electrical and Electronics Engineers
• Subjects: 6D motion tracking; Acceleration; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Feature extraction; Gesture recognition; Hidden Markov models; Memory organisation. Data processing; motion gesture; Optical sensors; Pattern recognition. Digital image processing. Computational geometry; Software; Tracking; Trajectory; Statistical analysis; Tracking; Motion estimation; Gesture recognition; motion gesture; Markov model; Modeling; Efficiency; Classification; Gesture; Database; Hidden Markov model; Selection criterion; Monitoring control system; 6D motion tracking
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2012.2237024

A 6D motion gesture is represented by a 3D spatial trajectory and augmented by another three dimensions of orientation. Using different tracking technologies, the motion can be tracked explicitly with the position and orientation or implicitly with the acceleration and angular speed. In this work, we address the problem of motion gesture recognition for command-and-control applications. Our main contribution is to investigate the relative effectiveness of various feature dimensions for motion gesture recognition in both user-dependent and user-independent cases. We introduce a statistical feature-based classifier as the baseline and propose an HMM-based recognizer, which offers more flexibility in feature selection and achieves better performance in recognition accuracy than the baseline system. Our motion gesture database which contains both explicit and implicit motion information allows us to compare the recognition performance of different tracking signals on a common ground. This study also gives an insight into the attainable recognition rate with different tracking devices, which is valuable for the system designer to choose the proper tracking technology.