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Patrick H. Winston. Otašević, Nikola (Nikola B.) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-03-06T15:43:50Z 2014-03-06T15:43:50Z 2013 2013 http://hdl.handle.net/1721.1/85463 870969410 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 95-97). The human visual system represents a very complex and important part of brain activity, occupying a very significant portion of the cortex resources. It enables us to see colors, detect motion, perceive dimensions and distance. It enables us to solve a very wide range of problems such as image segmentation, object tracking, as well as object and activity recognition. We perform these tasks so easily that we are not even aware of their enormous complexity. How do we do that? This question has motivated decades of research in the field of computer vision. In this thesis, I make a contribution toward solving the particular problem of visionbased human-action recognition by exploiting the compositional nature of simple actions such as running, walking or bending. Noting that simple actions consist of a series of atomic movements and can be represented as a structured sequence of poses, I designed and implemented a system that learns a model of actions based on human-pose classification from a single frame and from a model of transitions between poses through time. The system comprises three parts. The first part is the pose classifier that is capable of inferring a pose from a single frame. Its role is to take as input an image and give its best estimate of the pose in that image. The second part is a hidden Markov model of the transitions between poses. I exploit structural constraints in human motion to build a model that corrects some of the errors made by the independent single-frame pose classifier. Finally, in the third part, the corrected sequence of poses is used to recognize action based on the frequency of pose patterns, the transitions between the poses and hidden Markov models of individual actions. I demonstrate and test my system on the public KTH dataset, which contains examples of running, walking, jogging, boxing, handclapping, and handwaving, as well as on a new dataset, which contains examples of not only running and walking, but also jumping, crouching, crawling, kicking a ball, passing a basketball, and shooting a basketball. On these datasets, my system exhibits 91% action recognition recall rate. by Nikola Otasevic. M.Eng. 97 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Recognizing simple human actions by exploiting regularities in pose sequences Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f42776c6-c426-4214-8d15-f098e7aea45f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Recognizing simple human actions by exploiting regularities in pose sequences</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Otašević, Nikola (Nikola B.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The human visual system represents a very complex and important part of brain activity, occupying a very significant portion of the cortex resources. It enables us to see colors, detect motion, perceive dimensions and distance. It enables us to solve a very wide range of problems such as image segmentation, object tracking, as well as object and activity recognition. We perform these tasks so easily that we are not even aware of their enormous complexity. How do we do that? This question has motivated decades of research in the field of computer vision. In this thesis, I make a contribution toward solving the particular problem of visionbased human-action recognition by exploiting the compositional nature of simple actions such as running, walking or bending. Noting that simple actions consist of a series of atomic movements and can be represented as a structured sequence of poses, I designed and implemented a system that learns a model of actions based on human-pose classification from a single frame and from a model of transitions between poses through time. The system comprises three parts. The first part is the pose classifier that is capable of inferring a pose from a single frame. Its role is to take as input an image and give its best estimate of the pose in that image. The second part is a hidden Markov model of the transitions between poses. I exploit structural constraints in human motion to build a model that corrects some of the errors made by the independent single-frame pose classifier. Finally, in the third part, the corrected sequence of poses is used to recognize action based on the frequency of pose patterns, the transitions between the poses and hidden Markov models of individual actions. I demonstrate and test my system on the public KTH dataset, which contains examples of running, walking, jogging, boxing, handclapping, and handwaving, as well as on a new dataset, which contains examples of not only running and walking, but also jumping, crouching, crawling, kicking a ball, passing a basketball, and shooting a basketball. On these datasets, my system exhibits 91% action recognition recall rate.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>