  We propose a semi-automatic bounding box annotation method for visual object
tracking by utilizing temporal information with a tracking-by-detection
approach. For detection, we use an off-the-shelf object detector which is
trained iteratively with the annotations generated by the proposed method, and
we perform object detection on each frame independently. We employ Multiple
Hypothesis Tracking (MHT) to exploit temporal information and to reduce the
number of false-positives which makes it possible to use lower objectness
thresholds for detection to increase recall. The tracklets formed by MHT are
evaluated by human operators to enlarge the training set. This novel
incremental learning approach helps to perform annotation iteratively. The
experiments performed on AUTH Multidrone Dataset reveal that the annotation
workload can be reduced up to 96% by the proposed approach.
