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
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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
STARS Citation
Nagaraja, Adarsh, "Feature Pruning For Action Recognition In Complex Environment" (2011). Electronic Theses and Dissertations. 1875.
https://stars.library.ucf.edu/etd/1875