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.

本论文提出了一种半自动化的包围盒标注方法，利用跟踪 - 检测方法和时间信息，采用现成的目标检测器训练，利用多重假设追踪 (MHT) 来提高检测结果的可靠性，并使用人工评估来进行迭代标注。通过实验显示，该方法可以将标注工作量减少 96%。