  Autonomous driving systems require huge amounts of data to train. Manual
annotation of this data is time-consuming and prohibitively expensive since it
involves human resources. Therefore, active learning emerged as an alternative
to ease this effort and to make data annotation more manageable. In this paper,
we introduce a novel active learning approach for object detection in videos by
exploiting temporal coherence. Our active learning criterion is based on the
estimated number of errors in terms of false positives and false negatives. The
detections obtained by the object detector are used to define the nodes of a
graph and tracked forward and backward to temporally link the nodes. Minimizing
an energy function defined on this graphical model provides estimates of both
false positives and false negatives. Additionally, we introduce a synthetic
video dataset, called SYNTHIA-AL, specially designed to evaluate active
learning for video object detection in road scenes. Finally, we show that our
approach outperforms active learning baselines tested on two datasets.
