智能技术学报

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稿件标题: Sequential Bag-of-Words model for human action classification
稿件作者: Hong Liu,Hao Tang,Wei Xiao,ZiYi Guo,Lu Tian,Yuan Gao
关键字词: Action classification; Sequential Bag-of-Words; STIP; Probalibity
文章摘要: Recently, approaches utilizing spatial-temporal features to form Bag-of-Words (BoWs) models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub-actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our appr
收录刊物: 2016年1卷2期
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