A New Adaptive Segmental Matching Measure for Human Activity Recognition
Abstract
The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignmentbased method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.
Cite
Text
Shariat and Pavlovic. "A New Adaptive Segmental Matching Measure for Human Activity Recognition." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.445Markdown
[Shariat and Pavlovic. "A New Adaptive Segmental Matching Measure for Human Activity Recognition." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/shariat2013iccv-new/) doi:10.1109/ICCV.2013.445BibTeX
@inproceedings{shariat2013iccv-new,
title = {{A New Adaptive Segmental Matching Measure for Human Activity Recognition}},
author = {Shariat, Shahriar and Pavlovic, Vladimir},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.445},
url = {https://mlanthology.org/iccv/2013/shariat2013iccv-new/}
}