In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images

Farzan Erlik Nowruzi, Wassim A. El Ahmar, Robert Laganiere, Amir H. Ghods; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Counting people is a growing field of interest for researchers in recent years. In-vehicle passenger counting is an interesting problem in this domain that has several applications including High Occupancy Vehicle (HOV) lanes. In this paper, present a new in-vehicle thermal image dataset. We propose a tiny convolutional model to count on-board passengers and compare it to well known methods. We show that our model surpasses state-of-the-art methods in classification and has comparable performance in detection. Moreover, our model outperforms the state-of-the-art architectures in terms of speed, making it suitable for deployment on embedded platforms. We present the results of multiple deep learning models and thoroughly analyze them.

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[bibtex]
@InProceedings{Nowruzi_2019_CVPR_Workshops,
author = {Erlik Nowruzi, Farzan and El Ahmar, Wassim A. and Laganiere, Robert and Ghods, Amir H.},
title = {In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}