Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Crisostomo Romero, Pedro Moises

Contributors dc:contributor

• Forsyth, David A.

• Hand detection
• computer vision
• machine learning
• deformable models
• PASCAL Visual Object Classes (VOC)
• frequency features
• color features
• Pattern Analysis Statistical Modeling and Computational Learning (PASCAL)

Statement dc:rights

• Copyright 2011 Pedro Crisostomo

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/24270

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/24270