  Transferring image-based object detectors to the domain of video remains
challenging under resource constraints. Previous efforts utilised optical flow
to allow unchanged features to be propagated, however, the overhead is
considerable when working with very slowly changing scenes from applications
such as surveillance. In this paper, we propose temporal early exits to reduce
the computational complexity of per-frame video object detection. Multiple
temporal early exit modules with low computational overhead are inserted at
early layers of the backbone network to identify the semantic differences
between consecutive frames. Full computation is only required if the frame is
identified as having a semantic change to previous frames; otherwise, detection
results from previous frames are reused. Experiments on CDnet show that our
method significantly reduces the computational complexity and execution of
per-frame video object detection up to $34 \times$ compared to existing methods
with an acceptable reduction of 2.2\% in mAP.
