  The cost of drawing object bounding boxes (i.e. labeling) for millions of
images is prohibitively high. For instance, labeling pedestrians in a regular
urban image could take 35 seconds on average. Active learning aims to reduce
the cost of labeling by selecting only those images that are informative to
improve the detection network accuracy. In this paper, we propose a method to
perform active learning of object detectors based on convolutional neural
networks. We propose a new image-level scoring process to rank unlabeled images
for their automatic selection, which clearly outperforms classical scores. The
proposed method can be applied to videos and sets of still images. In the
former case, temporal selection rules can complement our scoring process. As a
relevant use case, we extensively study the performance of our method on the
task of pedestrian detection. Overall, the experiments show that the proposed
method performs better than random selection. Our codes are publicly available
at www.gitlab.com/haghdam/deep_active_learning.
