  Object detection is one of the most important and fundamental aspects of
computer vision tasks, which has been broadly utilized in pose estimation,
object tracking and instance segmentation models. To obtain training data for
object detection model efficiently, many datasets opt to obtain their
unannotated data in video format and the annotator needs to draw a bounding box
around each object in the images. Annotating every frame from a video is costly
and inefficient since many frames contain very similar information for the
model to learn from. How to select the most informative frames from a video to
annotate has become a highly practical task to solve but attracted little
attention in research. In this paper, we proposed a novel active learning
algorithm for object detection models to tackle this problem. In the proposed
active learning algorithm, both classification and localization informativeness
of unlabelled data are measured and aggregated. Utilizing the temporal
information from video frames, two novel localization informativeness
measurements are proposed. Furthermore, a weight curve is proposed to avoid
querying adjacent frames. Proposed active learning algorithm with multiple
configurations was evaluated on the MuPoTS dataset and FootballPD dataset.
