Large scale real-life action recognition using conditional random fields with stochastic training

Authors:
Xu Sun;Hisashi Kashima;Ryota Tomioka;Naonori Ueda
Affiliations:
Department of Mathematical Informatics, The University of Tokyo;Department of Mathematical Informatics, The University of Tokyo;Department of Mathematical Informatics, The University of Tokyo;NTT Communication Science Laboratories, Kyoto, Japan
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

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Abstract

Action recognition is usually studied with limited lab settings and a small data set. Traditional lab settings assume that the start and the end of each action are known. However, this is not true for the real-life activity recognition, where different actions are present in a continuous temporal sequence, with their boundaries unknown to the recognizer. Also, unlike previous attempts, our study is based on a large-scale data set collected from real world activities. The novelty of this paper is twofold: (1) Large-scale non-boundary action recognition; (2) The first application of the averaged stochastic gradient training with feedback (ASF) to conditional random fields. We find the ASF training method outperforms a variety of traditional training methods in this task.