happyness respectively. Frame-rate approaches 89 FPS.

**TKD with our key frame selection method:** Initially, we set $\tau$ (the training prevention factor) to $2$ (We observe that the transition between two scenes takes at least $2$ frames); along with setting the minimum random selection to $5\%$. In the Hollywood dataset, our method selects around $26\%$ of frames and the $F_1$ score achieves $0.76$ (IOU=0.5). In the pursuit of happyness movie, our method selects around $24\%$ of frames and the $F_1$ score reaches to $0.67$ (IOU=$0.5$). On average, the system achieves a frame-rate of $91$ FPS sequentially and $220$ FPS with running inference and knowledge distillation in parallel.

Table \[TKD\_hollywood\] lists the experimental results we observed with these variants. These experiments show, the TKD, while maintaining a similar frame-rate as other methods, it can achieve higher recognition accuracy. To further validate this claim, we conduct one additional experiment on a single-shot movie [@fish2013], TKD selects $21\%$ and random procedure selects $27\%$ of the total frames for re-training. They reach comparable F1-score (TKD:$0.807$, Random:$0.812$), but our TKD method uses 10400 frames less than the random one.

[ccc]{} &\
& & F-1 score\
& &\
& &\
& &\
& &\
& &\
\
