Key Point-Based Driver Activity Recognition
Abstract
We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.
Cite
Text
Vats and Anastasiu. "Key Point-Based Driver Activity Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00370Markdown
[Vats and Anastasiu. "Key Point-Based Driver Activity Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/vats2022cvprw-key/) doi:10.1109/CVPRW56347.2022.00370BibTeX
@inproceedings{vats2022cvprw-key,
title = {{Key Point-Based Driver Activity Recognition}},
author = {Vats, Arpita and Anastasiu, David C.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {3273-3280},
doi = {10.1109/CVPRW56347.2022.00370},
url = {https://mlanthology.org/cvprw/2022/vats2022cvprw-key/}
}