Style Transfer for Detecting Vehicles with Thermal Camera
Sebastian Cygert, Andrzej Czyżewski · 2019
In this work we focus on nighttime vehicle detection for intelligent traffic monitoring from the thermal camera. To train a Convolutional Neural Network (CNN) detector we create a stylized version of COCO (Common Objects in Context) dataset using Style Transfer technique that imitates images obtained from thermal cameras. This new dataset is further used for fine-tuning of the model and as a result detection accuracy on images from thermal cameras has significantly improved. As a side effect, we noticed that Style Transfer can be also used to improve detection accuracy from standard RGB camera, which has potential for various applications.