Keywords

Cluster analysis, Computer vision, Human activity recognition, Pattern recognition systems, Support vector machines, Video recordings

Abstract

A significant number of action recognition research efforts use spatio-temporal interest point detectors for feature extraction. Although the extracted features provide useful information for recognizing actions, a significant number of them contain irrelevant motion and background clutter. In many cases, the extracted features are included as is in the classification pipeline, and sophisticated noise removal techniques are subsequently used to alleviate their effect on classification. We introduce a new action database, created from the Weizmann database, that reveals a significant weakness in systems based on popular cuboid descriptors. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning the system or selecting better interest points. Instead, we show that the problem lies at the descriptor level and must be addressed by modifying descriptors.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2011

Semester

Summer

Advisor

Tappen, Marshall

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Format

application/pdf

Identifier

CFE0003882

URL

http://purl.fcla.edu/fcla/etd/CFE0003882

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

Download

Share

COinS