期刊
COMPUTER VISION AND IMAGE UNDERSTANDING
卷 113, 期 10, 页码 1064-1075出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2009.06.002
关键词
Human motion recognition; Recursive filtering; Support vector machine
资金
- National Science Foundation [IIS-0219863, IIS-0208621, IIS-0534286, CNS-0224363, CNS-0324864, CNS-0420836, IIP-0443945, IIP-0726109, CNS-0708344, CNS-0821474]
- US Army Research Laboratory
- US Army Research Office [911NF-08-1-0463]
- Minnesota Department of Transportation
- ITS Institute at the University of Minnesota
We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets. (C) 2009 Elsevier Inc. All rights reserved.
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