Shi, QinfengWang, LiCheng , LiSmola, Alexander2015-12-10June 24-269781424422432http://hdl.handle.net/1885/54606Given an input video sequence of one person conducting a sequence of continuous actions, we consider the problem of jointly segmenting and recognizing actions. We propose a discriminative approach to this problem under a semi-Markov model framework, where we are able to define a set of features over input-output space that captures the characteristics on boundary frames, action segments and neighboring action segments, respectively. In addition, we show that this method can also be used to recognize the person who performs in this video sequence. A Viterbi-like algorithm is devised to help efficiently solve the induced optimization problem. Experiments on a variety of datasets demonstrate the effectiveness of the proposed method.Keywords: Artificial intelligence; Computer vision; Feature extraction; Image processing; Imaging techniques; Pattern recognition; Photography; Video recording; Viterbi algorithm; Continuous actions; Data-sets; Discriminative approach; Human actions; Input video se200810.1109/CVPR.2008.45875572015-12-09