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dc.contributor.advisorGhanem, Bernard
dc.contributor.authorAlwassel, Humam
dc.date.accessioned2018-04-29T06:44:10Z
dc.date.available2019-04-17T00:00:00Z
dc.date.issued2018-04-17
dc.identifier.citationAlwassel, H. (2018). Efficient Temporal Action Localization in Videos. KAUST Research Repository. https://doi.org/10.25781/KAUST-U2I04
dc.identifier.doi10.25781/KAUST-U2I04
dc.identifier.urihttp://hdl.handle.net/10754/627678
dc.description.abstractState-of-the-art temporal action detectors inefficiently search the entire video for specific actions. Despite the encouraging progress these methods achieve, it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched. To address this need, we propose the new problem of action spotting in videos, which we define as finding a specific action in a video while observing a small portion of that video. Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in a video, we propose Action Search, a novel Recurrent Neural Network approach that mimics the way humans spot actions. Moreover, to address the absence of data recording the behavior of human annotators, we put forward the Human Searches dataset, which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets. We consider temporal action localization as an application of the action spotting problem. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently (observing on average 17.3% of the video) but it also accurately finds human activities with 30.8% mAP (0.5 tIoU), outperforming state-of-the-art methods
dc.language.isoen
dc.subjectvideo understanding
dc.subjectaction localization
dc.subjectaction spotting
dc.titleEfficient Temporal Action Localization in Videos
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.rights.embargodate2019-04-17
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHeidrich, Wolfgang
dc.contributor.committeememberWonka, Peter
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
dc.rights.accessrightsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2019-04-17.
refterms.dateFOA2019-04-17T00:00:00Z


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