Model-Based Gait and Action Recognition Using Kinect
Abstract
Being the very first in the category of low-cost consumer-level depth sensors, the recent release of Microsoft Kinect has opened the door to a new generation of computer vision and biometric security applications. This thesis focuses on designing new methodologies for Kinect-based gait and action recognition systems that utilize the Kinect 3D virtual skeleton to construct effective and robust motion representations. The proposed gait recognition method focuses on designing a feature descriptor that can capture person-specific distinct motion patterns, caused by the influence of human physiology and behavioral traits. On the other hand, the proposed action recognition method involves constructing a person-independent feature descriptor that can suppress person-specific motion traits while highlighting a more generic and high level description of action-specific skeletal joint movements. Extensive experiments with three recently released public benchmark databases demonstrate the effectiveness of the proposed methodologies, compared against state-of-the-art gait and action recognition methods.
Description
Keywords
Computer Science
Citation
Ahmed, F. (2016). Model-Based Gait and Action Recognition Using Kinect (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26931