ML Anthology

Efficient Activity Detection with Max-Subgraph Search

Chao-Yeh Chen, Kristen Grauman
CVPR 2012 pp. 1274-1281
doi:10.1109/CVPR.2012.6247811 /cvpr/2012/chen2012cvpr-efficient/

Abstract

We propose an efficient approach that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem over a learned space-time graph constructed on the test sequence. We show this permits an efficient branch-and-cut solution for the best-scoring - and possibly non-cubically shaped - portion of the video for a given activity classifier. The upshot is a fast method that can evaluate a broader space of candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on three datasets, and show its speed and accuracy advantages over multiple existing search strategies.

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Cite

Text

Chen and Grauman. "Efficient Activity Detection with Max-Subgraph Search." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247811

Markdown

[Chen and Grauman. "Efficient Activity Detection with Max-Subgraph Search." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/chen2012cvpr-efficient/) doi:10.1109/CVPR.2012.6247811

BibTeX

@inproceedings{chen2012cvpr-efficient,
  title     = {{Efficient Activity Detection with Max-Subgraph Search}},
  author    = {Chen, Chao-Yeh and Grauman, Kristen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2012},
  pages     = {1274-1281},
  doi       = {10.1109/CVPR.2012.6247811},
  url       = {https://mlanthology.org/cvpr/2012/chen2012cvpr-efficient/}
}