\[sec:intro\], the overall objective of our system is to estimate the expectation of object labels, their sizes, and shapes on the temporal domain and to improve the performance of the student model. Following this intuition, we put forward a mechanism with a combination of an oracle model (which we consider it as the best possible model) and a student model (which is fast but has considerably lower accuracy compared to the oracle). We are transferring the temporal knowledge of the oracle model to the student model at the . By transferring this knowledge, the student model adapts itself to the current environment or scene. Without loss of generality, We select Yolo-v3 object detection model as the oracle model due to its reliable and dominating performance compared with other one-stage methods. We select Tiny-Yolo model [@redmon2018yolov3] as the student model due to its high base frame rate and having a similar model structure with the Yolo-v3.

The TKD Architecture
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We show our overall framework in Figure \[TKD\_fig\]. In the student model, we include two decoders as the TKD decoder and the general decoder. Then, the pre-trained Yolo-v3 [@redmon2018yolov3] is adopted as the oracle. We run the Oracle model with the 