@inproceedings{cd162493711645eabee4a15a424ba1c7,
title = "Towards a low-cost embedded vehicle counting system based on deep-learning for traffic management applications",
abstract = "This paper explores the feasibility of using a low-cost embedded system for real-time vehicle detection and counting through the use of deep neural networks. It compares the performance of two different object tracking methods, the Kalman filter with the Hungarian algorithm and the centroid tracking algorithm. The experimentation proved that the efficiency of the implemented algorithms was above the 92\% and 98\% for the centroid tracking algorithm and Kalman filter with the Hungarian algorithm, respectively. Also, the Kalman filter produced fewer errors overcoming the centroid tracking algorithm.",
keywords = "Centroid, Deep-learning, Kalman filter, Neural computer stick (NCS2), Raspberry Pi, Tracking, Vehicle recognition, YOLOv4 tiny",
author = "Josue Navarro and Benitez, \{Diego S.\} and Noel Perez and Daniel Riofrio and Moyano, \{Ricardo Flores\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 ; Conference date: 06-12-2021 Through 09-12-2021",
year = "2021",
doi = "10.1109/CHILECON54041.2021.9702914",
language = "Ingl{\'e}s",
series = "2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021",
}