train the student model during the inference stage. Specifically, TKD seeks the frames that by training over them the model has a higher expectation of reducing the loss, thus eventually improves the detection accuracy. For the rest of the paper, we denote these frames as the key frames.

Selecting a larger number of frames as the key frames will hurt the performance since re-training is computationally expensive; While selecting too few number of frames will hurt the detection accuracy as the student may not align well with the oracle model in time. Thus, an effective and fast procedure to select the key frames is highly desired to yield a positive effect on the system’s performance.

We propose a key frame selection procedure which is both efficient and also practical. First, we check the training prevention factor $\tau$. If the student model has been trained in any last $\tau$ frames; we will exit the key selection procedure. It is based on the reasonable assumption that if we have an environment change, it typically takes $\tau$ frames that this change to be fully observable. Thus, when we train the student, training for the next $\tau$ frames would not be beneficiary. Second, we 