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Style Transfer for Detecting Vehicles with Thermal Camera

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.