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Action recognition by hidden temporal models
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PRE
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DOI：10.1007/s00371-013-0899-9.png)
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We focus on the recognition of human actions in uncontrolled videos that may contain complex temporal structures. It is a difficult problem because of the large intra-class variations in viewpoint, video length, motion pattern, etc. To address these difficulties, we propose a novel system in this paper that represents each action class by hidden temporal models. In this system, we represent the crucial action event per category by a video segment that covers a fixed number of frames and can move temporally within the sequences. To capture the temporal structures, the video segment is described by a temporal pyramid model. To capture large intra-class variations, multiple models are combined using Or operation to represent alternative structures. The index ofmodel and the start frame of segment are both treated as hidden variables. We implement a learning procedure based on the latent SVM method. The proposed approach is tested on two difficult benchmarks: the Olympic Sports and HMDB51 data sets. The experimental results reveal that our system is comparable to the state-of-the-art methods in the literature.
Keyword:
Human action recognition
Temporal pyramid model (TPM)
Multi-model representation
Latent SVM
期刊
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2.9
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4.6K
被引数:
6.5K
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