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

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Title: Hand detection on images based on deformable part models and additional features
Author(s): Crisostomo Romero, Pedro Moises
Advisor(s): Forsyth, David A.
Department / Program: Computer Science
Discipline: Computer Science
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(s): Hand detection computer vision machine learning deformable models PASCAL VOC frequency features color features.
Abstract: 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.
Issue Date: 2011-05-25
URI: http://hdl.handle.net/2142/24270
Rights Information: Copyright 2011 Pedro Crisostomo
Date Available in IDEALS: 2011-05-25
Date Deposited: 2011-05
 

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