This repository provides the implementation of our paper Automated training of location-specific edge models for traffic counting. The goal of this paper is to count multiple traffic modalities (car, cyclist, pedestrians, and others) with a model that is as small as possible while maintaining a high accuracy. We experimentally show that we achieve similar results as the SToA counting methods with 5x fewer parameters.
- Clone this repo and prepare the environment
git clone https://github.com/lyn1874/efficient_traffic_count_on_edge_devices.git
cd efficient_traffic_count_on_edge_devices
./requirement.shpython3 inference.py --compound_coef 0 --skip 4- https://www.youtube.com/watch?v=MNn9qKG2UFI&t=6s
- https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
- traffic counting tutorial
- update clean code
- convert model to onnx
- convert model to coreml
- debug the coreml model
- deploy the algorithm on Jetson, report the inference speed
- simulate the online streaming input
If you use our code, please cite
@article{LEROUX2022107763,
title = {Automated training of location-specific edge models for traffic counting},
journal = {Computers & Electrical Engineering},
volume = {99},
pages = {107763},
year = {2022},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2022.107763},
url = {https://www.sciencedirect.com/science/article/pii/S0045790622000672},
author = {Sam Leroux and Bo Li and Pieter Simoens},
}