  In training object detector based on convolutional neural networks, selection
of effective positive examples for training is an important factor. However,
when training an anchor-based detectors with sparse annotations on an image,
effort to find effective positive examples can hinder training performance.
When using the anchor-based training for the ground truth bounding box to
collect positive examples under given IoU, it is often possible to include
objects from other classes in the current training class, or objects that are
needed to be trained can only be sampled as negative examples. We used two
approaches to solve this problem: 1) the use of an anchorless object detector
and 2) a semi-supervised learning-based object detection using a single object
tracker. The proposed technique performs single object tracking by using the
sparsely annotated bounding box as an anchor in the temporal domain for
successive frames. From the tracking results, dense annotations for training
images were generated in an automated manner and used for training the object
detector. We applied the proposed single object tracking-based semi-supervised
learning to the Epic-Kitchens dataset. As a result, we were able to achieve
\textbf{runner-up} performance in the Unseen section while achieving the first
place in the Seen section of the Epic-Kitchens 2020 object detection challenge
under IoU > 0.5 evaluation
