Towards Large-Scale Video Video Object Mining
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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.
@article{OsepVoigtlaender18ECCVW,
title={Towards Large-Scale Video Object Mining},
author={Aljo\v{s}a O\v{s}ep and Paul Voigtlaender and Jonathon Luiten and Stefan Breuers and Bastian Leibe},
journal={ECCV 2018 Workshop on Interactive and Adaptive Learning in an Open World},
year={2018}
}