ML Anthology

Discriminative Human Action Segmentation and Recognition Using Semi-Markov Model

Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola
CVPR 2008
doi:10.1109/CVPR.2008.4587557 /cvpr/2008/shi2008cvpr-discriminative/

Abstract

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 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.

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Cite

Text

Shi et al. "Discriminative Human Action Segmentation and Recognition Using Semi-Markov Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587557

Markdown

[Shi et al. "Discriminative Human Action Segmentation and Recognition Using Semi-Markov Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/shi2008cvpr-discriminative/) doi:10.1109/CVPR.2008.4587557

BibTeX

@inproceedings{shi2008cvpr-discriminative,
  title     = {{Discriminative Human Action Segmentation and Recognition Using Semi-Markov Model}},
  author    = {Shi, Qinfeng and Wang, Li and Cheng, Li and Smola, Alexander J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587557},
  url       = {https://mlanthology.org/cvpr/2008/shi2008cvpr-discriminative/}
}