Detection:**This method uses the content-aware scene detection method [@castellano2018pyscenedetect]. It finds areas where the difference between two subsequent frames exceeds the threshold value and used them as key frames for training the student. We selected the threshold with the highest performance and accuracy to report. This method achieves $0.58$ $F_1$ score and $0.58$ $F_1$ score in the Hollywood scene dataset and The pursuit of happyness respectively. This method selected $24\%$ frames as key frames ultimately. On average, the system yields a $93$ FPS.

**Tiny-Yolo without any training:** We test Tiny-Yolo [@redmon2018yolov3] to show the accuracy of a strong baseline model without temporal knowledge distillation. This model achieves $0.16$ $F_1$ score and $0.11$ $F_1$ score in the Hollywood scene dataset and The pursuit of happyness respectively, which are significantly lower than the other mentioned methods. However, This model has $220$ FPS, the fastest among all.

**Tiny-Yolo $+$ Yolo-v3 without training:** In this configuration, we used Tiny-Yolo and Yolo-v3 v3 [@redmon2018yolov3] together. We designed a random procedure which runs Yolo-v3 with a probability of $27\%$ and Tiny-Yolo for the rest of the times. This model achieves $0.49$ $F_1$ score and $0.47$ $F_1$ score in the Hollywood scene dataset and the pursuit of 