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Title:Human Activity Recognition with Metric Learning
Author(s):Tran, Du; Sorokin, Alexander; Forsyth, David A.
Subject(s):machine learning
Abstract:This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We show that our approach outperforms all state-of-the-art methods on numerous standard datasets for traditional action classification problem. Furthermore, we demonstrate that our method not only can accurately label activities but also can reject unseen activities and can learn from few examples with high accuracy. We finally show that our approach works well on noisy YouTube videos.
Issue Date:2008-03
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2008-2952
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-22

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