Human activity recognition and gymnastics analysis through depth imagery

Depth imagery is transforming many areas of computer vision, such as object recognition, human detection, human activity recognition, and sports analysis. The goal of my work is twofold: (1) use depth imagery to effectively analyze the pommel horse event in men’s gymnastics, and (2) explore and build upon the use of depth imagery to recognize human activities through skeleton representation. I show that my gymnastics analysis system can accurately segment a scene based on depth to identify a ‘depth of interest’, ably recognize activities on the pommel horse using only the gymnast’s silhouette, and provide an informative analysis of the gymnast’s performance. This system runs in real-time on an inexpensive laptop, and has been built into an application in use by elite gymnastics coaches. Furthermore, I present my work expanding on a bio-inspired skeleton representation obtained through depth data. This representation outperforms existing methods in classification accuracy on benchmark datasets. I then show that it can be used to interact in real-time with a Baxter humanoid robot, and is more accurate at recognizing both complete and ongoing interactions than current state-of-the-art methods.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Grantor dc:publisher

Colorado School of Mines. Arthur Lakes Library

Year dc:date.issued

2016

Author dc:creator

• Reily, Brian J.

Advisors dc:contributor.advisor

• Hoff, William A.
• Zhang, Hao

Committee members dc:contributor.committeemember

• Wang, Hua
• Celik, Ozkan

• activity prediction
• activity recognition
• depth imagery
• gymnastics
• image segmentation

Statement dc:rights

• Copyright of the original work is retained by the author.

Language dc:language.iso

eng, English

Identifier

T 8032

OAI identifier oai:identifier

oai:repository.mines.edu:11124/170153