Greg Mori
生駒 : 奈良先端科学技術大学院大学, 2013.5
Lecture Archive
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Developing computer vision algorithms to interpret scenes of human activity involves a number of related tasks including human detection, tracking, and action recognition. These tasks are intertwined, information from one can provide assist in solving others. In this talk we will describe discriminative latent variable models to address these tasks together, focusing on the latent SVM / max-margin hidden conditional random field. We will present methods for jointly recognizing actions and spatio-temporally localizing them in videos. Models for human-human and human-object interactions will be presented. We will present methods for group activity recognition, with holistic analysis of entire scenes of people interacting and taking different social roles.
2013
電子化映像資料(1時間21分39秒)
情報科学研究科・ゼミナール講演 ; 平成25年度
講演者所属: Simon Fraser University
講演日: 平成25年5月27日
講演場所: 情報科学研究科大講義室L1
Japan
English (eng)
English (eng)
