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UBC Theses and Dissertations

Towards human pose estimation in video sequences Oleinikov, Georgii


Recent advancements in human pose estimation from single images have attracted wide scientific interest of the Computer Vision community to the problem domain. However, the problem of pose estimation from monocular video sequences is largely under-represented in the literature despite the wide range of its applications, such as action recognition and human-computer interaction. In this thesis we present two novel algorithms for video pose estimation that demonstrate how one could improve the performance of a state-of-the-art single-image articulated human detection algorithm on realistic video sequences. Furthermore, we release the UCF Sports Pose dataset, containing full-body pose annotations of people performing various actions in realistic videos, together with a novel pose evaluation metric that better reflects the performance of current state of the art. We also release the Video Pose Annotation tool, a highly customizable application that we used to construct the dataset. Finally, we introduce a task-based abstraction for human pose estimation, which selects the "best" algorithm for every specific instance based on a task description defined using an application programming interface covering the large volume of the human pose estimation domain.

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