In Vehicle Occupancy Detection With Convolutional Networks on Thermal Images
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.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)