如果一个人做了一系列连续动作，并被拍摄成一段视频，那么如何通过这段视频对动作进行分割和识别是人们要考虑的问题。为了对视频中的人的动作进行有效识别，基于半马尔可夫模型框架，提出了一个对人的动作进行识别的方法，该方法通过输入-输出空间的一组特征值来抓住与2个动作相邻的帧的特征，以及相邻的2个动作段之间的特征。为了提高算法的效率，提出了一个类似于Viterbi的算法，该算法被用来解决优化问题。不同数据集上的实验结果表明，该方法是有效的。

Given an input video sequence of one person who conducted a sequence of continuous actions,we consider the problem of jointly segmenting and recognizing actions.To recognize the activities in videos,we propose a discriminative approach to this problem within 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.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.