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Discriminative Latent Variable Models for Human Action Recognition MPMeister

Greg Mori

生駒 : 奈良先端科学技術大学院大学, 2013.5

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Holdings:
  Volume Printing year Location Call Number Material ID Circulation class Status Waiting
1 MPMeister   Digital Library LA-I-R[Flash][Mobile] M010920 Regular 0
Contents Intro. : 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.
Publication year : 2013
Form : 電子化映像資料(1時間21分39秒)
Series title :

情報科学研究科・ゼミナール講演 ; 平成25年度

Note :

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

Country of publication : Japan
Title language : English (eng)
Language of texts : English (eng)
Author information :

Mori, Greg