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|Title:||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.|
|Appears in Collections:||Electronic Theses|
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|ucalgary_2016_ahmed_faisal.pdf||Thesis pdf file||1.99 MB||Adobe PDF||View/Open|
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