Im2Vid: Future Video Prediction for Static Image Action Recognition

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dc.contributor.authorAlBahar, Badour A Sh A.en
dc.contributor.committeechairHuang, Jia-Binen
dc.contributor.committeememberTokekar, Pratapen
dc.contributor.committeememberAbbott, A. Lynnen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2018-06-21T08:01:04Zen
dc.date.available2018-06-21T08:01:04Zen
dc.date.issued2018-06-20en
dc.description.abstractStatic image action recognition aims at identifying the action performed in a given image. Most existing static image action recognition approaches use high-level cues present in the image such as objects, object human interaction, or human pose to better capture the action performed. Unlike images, videos have temporal information that greatly improves action recognition by resolving potential ambiguity. We propose to leverage a large amount of readily available unlabeled videos to transfer the temporal information from video domain to static image domain and hence improve static image action recognition. Specifically, We propose a video prediction model to predict the future video of a static image and use the future predicted video to improve static image action recognition. Our experimental results on four datasets validate that the idea of transferring the temporal information from videos to static images is promising, and can enhance static image action recognition performance.en
dc.description.abstractgeneralStatic image action recognition is the problem of identifying the action performed in a given image. Most existing approaches use the high-level cues present in the image like objects, object human interaction, or human pose to better capture the action performed. Unlike images, videos have temporal information that greatly improves action recognition. Looking at a static image of a man who is about to sit on a chair might be misunderstood as an image of a man who is standing from the chair. Because of the temporal information in videos, such ambiguity is not present. To transfer the temporal information and action features from video domain to static image domain and hence improve static image action recognition, we propose a model that learns a mapping from a static image to its future video by looking at a large number of existing images and their future videos. We then use this model to predict the future video of a static image to improve its action recognition. Our experimental results on four datasets show that the idea of transferring the temporal information from videos to static images is promising, and can enhance static image action recognition performance.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:15485en
dc.identifier.urihttp://hdl.handle.net/10919/83602en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectHuman Action Recognitionen
dc.subjectStatic Image Action Recognitionen
dc.subjectVideo Action Recognitionen
dc.subjectFuture Video Predictionen
dc.titleIm2Vid: Future Video Prediction for Static Image Action Recognitionen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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