Discriminative human action segmentation and recognition using semi-Markov model

Given 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 inputoutput 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.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola
• Keywords: Action Segments, Computer Vision, CVPR 2008, Input Video Sequence, Optimization Problem, Semi-markov Model Framework