  We propose to leverage a generic object tracker in order to perform object
mining in large-scale unlabeled videos, captured in a realistic automotive
setting. We present a dataset of more than 360'000 automatically mined object
tracks from 10+ hours of video data (560'000 frames) and propose a method for
automated novel category discovery and detector learning. In addition, we show
preliminary results on using the mined tracks for object detector adaptation.
