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.

翻译：我们建议采用半自动捆绑盒注释法,利用时间信息进行视觉物体跟踪,采用跟踪和检测方法进行时间信息跟踪。为了检测,我们使用现成的物体探测器,该探测器与拟议方法生成的说明进行迭接培训,并在每个框架独立进行物体检测。我们使用多假假体跟踪(MHT)来利用时间信息,减少假药阳性的数量,从而有可能使用较低的物体临界值进行检测,从而增加回溯。由MHT形成的跟踪器由人类操作者进行评估,以扩大培训集。这种新型的渐进式学习方法有助于进行批注的迭接。在AUTH多德罗恩数据集上进行的实验表明,通过拟议方法,批注工作量可以减少到96%。