Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform
Abstract
Brain-inspired computer vision (BICV) has evolved rapidly in recent years and it is now competitive with traditional CV approaches. However, most of BICV algorithms have been developed on high power-and-performance platforms (e.g. workstations) or special purpose hardware. We propose two different algorithms for counting people in a classroom, both based on Convolutional Neural Networks (CNNs), a state-of-art deep learning model that is inspired on the structure of the human visual cortex. Furthermore, we provide a standalone parallel C library that implements CNNs and use it to deploy our algorithms on the embedded mobile ARM big. LITTLE-based Odroid-XU platform. Our performance and power measurements show that neuromorphic vision is feasible on off-the-shelf embedded mobile platforms, and we show that it can reach very good energy efficiency for non-time-critical tasks such as people counting.
Cite
Text
Conti et al. "Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.95Markdown
[Conti et al. "Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/conti2014cvprw-braininspired/) doi:10.1109/CVPRW.2014.95BibTeX
@inproceedings{conti2014cvprw-braininspired,
title = {{Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform}},
author = {Conti, Francesco and Pullini, Antonio and Benini, Luca},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2014},
pages = {624-629},
doi = {10.1109/CVPRW.2014.95},
url = {https://mlanthology.org/cvprw/2014/conti2014cvprw-braininspired/}
}