likelihood of objects which are more frequently found from the previous observations. Since the model requires online training during the inference stage, it should be able to address the following challenges:

1.  Training is a time consuming procedure, running it at the inference stage hurts model efficiency;

2.  Selecting the key frames accurately on which the student model needs to be adapted;

3.  Objects with low appearance probability may not be detected by the student model after adaptation;

4.  The oracle model still introduces noise at locations where there are no objects. Simply training the student model with noisy oracle output decreases the accuracy.

In the following section, we will introduce our approach to address these challenges respectively.

Our Approach {#sec:TKD_structure}
============

In this work, we adopt Yolo-v3 (as teacher) and Tiny-Yolo v3 (as student) [@redmon2018yolov3] as the base object detection methods. These two models are one-stage object detection models. In both models, object detection is conducted at various layers. The middle layers are used to detect large objects and the last layers to detect small objects. Studies [@redmon2018yolov3], [@mullapudi2018online] and [@lin2018focal] showed that this strategy successfully improves the object detection accuracy with a significant edge.

As mentioned in Section 