In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images
Farzan Erlik Nowruzi, Wassim El Ahmar, Robert Laganière, Amir Hosein Ghods · 2019
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.