  Deep neural networks based methods have been proved to achieve outstanding
performance on object detection and classification tasks. Despite significant
performance improvement, due to 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
vision 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 networks 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) an
Long-short Term Memory (LSTM) based key frame selection method; and 2) a novel
teacher-bounded loss design. To validate, 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, compare to other modern object recognition
methods.
