Evaluation Of Articulated Human Motion And Pose Estimation

Author : Suman Sedai
Publisher :
ISBN 13 :
Total Pages : 145 pages
Book Rating : 4.:/5 (93 download)

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Download or read book Human Motion Capture in Images and Videos Using Discriminative and Hybrid Methods written by Suman Sedai and published by . This book was released on 2012 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Truncated abstract] Vision-based human pose estimation and tracking is a popular research area that has generated a great deal of interest in the last decade. This is motivated by the fact that this research area has many applications including video surveillance, clinical rehabilitation and the analysis of athlete performance. It is also non-intrusive and does not require markers to be attached to the body parts, as opposed to the marker based motion capture systems. In this thesis, two machine learning and one feature representation techniques have been developed to automatically capture human motion from images and videos. This thesis is organized as a set of papers published to and/or under review by journals or international conferences. During the last two decades there has been much work in markerless human motion capture. This thesis contributes to the existing body of work by providing three new algorithms. First, an appearance descriptor is proposed for human pose estimation from monocular images. Second, a discriminative learning-based fusion algorithm is proposed to combine shape and appearance features for human pose estimation from monocular images. Third, a hybrid discriminative and generative method that takes into account prediction uncertainty of the discriminative model is proposed for 3D human pose tracking from both single and multiple cameras. Shape-based features such as silhouettes and appearance features are commonly used for pose estimation from monocular images using regression based techniques. Silhouette features require a segmentation step to obtain only information pertinent to the shape of the occluding body parts and discards appearance information that can potentially be useful for pose estimation. In order to utilize appearance information, we present an appearance descriptor that involves dimensionality reduction and vector quantization and that is suitable for regression-based human pose estimation. To objectively compare the state of art shape and appearance descriptors with our appearance descriptor, we conducted a quantitative evaluation using the HumanEva-I dataset. Shape-based features such as silhouettes are insensitive to background variations but they can be associated with more than one pose, resulting in ambiguities. Appearance features, on the other hand, can be more distinctive than shape features but they may be affected by background clutter and variations in the clothing of the human subject which can make appearance features unstable. While neither shape nor appearance features are self-sufficient for a robust estimation of human poses, they have the potential to complement each other because one may not be sensitive to conditions that affect the other. This thesis presents a novel fusion method based on discriminative learning to combine the proposed appearance descriptor with a shape descriptor to exploit their complementary properties for human pose estimation from monocular images. The proposed method, which is named localized decision level fusion technique, is based on clustering the output pose space into several partitions and learning a decision level fusion of the regression models for the shape and appearance descriptor in each region...