Real-Time Classification of Pedestrians and Cyclists for Intelligent Counting of Non-Motorized Traffic
Abstract
We propose a real-time method for counting pedestrians and bicyclists by classifying bulks of asynchronous events generated upon scene activities by an event-based 3D dynamic vision system. The inherent detection of moving objects offered by the 3D dynamic vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving objects. A clustering method exploits the sparse spatio-temporal representation of sensor's events for real-time detection and separation between moving objects. The method has been demonstrated for clustering the events and classification of pedestrian and cyclists moving across the sensor field of view based on their dimensions and passage duration. Tests on real scenarios with more than 100 cyclists and pedestrians yield a classification performance above 92%.
Cite
Text
Belbachir et al. "Real-Time Classification of Pedestrians and Cyclists for Intelligent Counting of Non-Motorized Traffic." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543170Markdown
[Belbachir et al. "Real-Time Classification of Pedestrians and Cyclists for Intelligent Counting of Non-Motorized Traffic." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/belbachir2010cvprw-realtime/) doi:10.1109/CVPRW.2010.5543170BibTeX
@inproceedings{belbachir2010cvprw-realtime,
title = {{Real-Time Classification of Pedestrians and Cyclists for Intelligent Counting of Non-Motorized Traffic}},
author = {Belbachir, Ahmed Nabil and Schraml, Stephan and Brändle, Norbert},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2010},
pages = {45-50},
doi = {10.1109/CVPRW.2010.5543170},
url = {https://mlanthology.org/cvprw/2010/belbachir2010cvprw-realtime/}
}