NDLI: Combining sparse and dense descriptors with temporal semantic structures for robust human action recognition

Content Provider	IEEE Xplore Digital Library
Author	Jie Chen Guoying Zhao Kellokumpu, V. Pietikainen, M.
Copyright Year	2011
Description	Author affiliation: Machine Vision Group, Department of Computer Science and Engineering, P. O. Box 4500 FI-90014 University of Oulu, Finland (Jie Chen; Guoying Zhao; Kellokumpu, V.; Pietikainen, M.)
Abstract	Automatic categorization of human actions in the real world is very challenging due to the great intra-class differences. In this paper, we present a new method for robust recognition of human actions. We first cluster each video in the training set into temporal semantic segments by a dense descriptor. Each segment in the training set is represented by a concatenated histogram of sparse and dense descriptors. These histograms of segments are used to train a classifier. In the recognition stage, a query video is also divided into temporal semantic segments by clustering. Each segment will obtain a confidence evaluated by the trained classifier. Combining the confidence of each segment, we classify this query video. To evaluate our approach, we perform experiments on two challenging datasets, i.e., the Olympic Sports Dataset (OSD) and Hollywood Human Action dataset (HOHA). We also test our method on the benchmark KTH human action dataset. Experimental results confirm that our algorithm performs better than the state-of-the-art methods.
Starting Page	1524
Ending Page	1531
File Size	1345207
Page Count	8
File Format	PDF
ISBN	9781467300629
e-ISBN	9781467300636
DOI	10.1109/ICCVW.2011.6130431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Histograms Motion segmentation Semantics Humans Object segmentation Indexes
Content Type	Text
Resource Type	Article

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