A Combination of Generative and Discriminative Models for Fast Unsupervised Activity Recognition from Traffic Scene Videos
Abstract
Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian inference to the training stage, where unsupervised clustering is performed to extract semantically meaningful activities from the scene. In the testing stage, we use discriminative classifiers, taking advantage of their relative simplicity and fast inference. Experiments on publicly available data-sets show that our approach is comparable in classification accuracy to state-of-the-art methods and provides a significant speed-up in the testing phase.
Cite
Text
Krishna and Denzler. "A Combination of Generative and Discriminative Models for Fast Unsupervised Activity Recognition from Traffic Scene Videos." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836042Markdown
[Krishna and Denzler. "A Combination of Generative and Discriminative Models for Fast Unsupervised Activity Recognition from Traffic Scene Videos." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/krishna2014wacv-combination/) doi:10.1109/WACV.2014.6836042BibTeX
@inproceedings{krishna2014wacv-combination,
title = {{A Combination of Generative and Discriminative Models for Fast Unsupervised Activity Recognition from Traffic Scene Videos}},
author = {Krishna, Mahesh Venkata and Denzler, Joachim},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {640-645},
doi = {10.1109/WACV.2014.6836042},
url = {https://mlanthology.org/wacv/2014/krishna2014wacv-combination/}
}