Deep neural network-based methods have been proved to achieve outstanding performance on object detection and classification tasks. Despite the significant performance improvement using the deep structures, they still require prohibitive runtime to process images and maintain the highest possible performance for real-time applications. Observing the phenomenon that human visual system (HVS) relies heavily on the temporal dependencies among frames from the visual input to conduct recognition efficiently, we propose a novel framework dubbed as TKD: temporal knowledge distillation. This framework distills the temporal knowledge from a heavy neural network-based model over selected video frames (the perception of the moments) to a light-weight model. To enable the distillation, we put forward two novel procedures: 1) a Long-short Term Memory (LSTM)-based keyframe selection method; and 2) a novel teacher-bounded loss design. To validate our approach, we conduct comprehensive empirical evaluations using different object detection methods over multiple datasets including Youtube-Objects and Hollywood scene dataset. Our results show consistent improvement in accuracy-speed trad- offs for object detection over the frames of the dynamic scene, compared to other modern object recognition methods. It can maintain the desired accuracy with the throughput of around 220 images per second. Implementation: https://github.com/mfarhadi/TKD-Cloud.