  Video object detection is challenging because objects that are easily
detected in one frame may be difficult to detect in another frame within the
same clip. Recently, there have been major advances for doing object detection
in a single image. These methods typically contain three phases: (i) object
proposal generation (ii) object classification and (iii) post-processing. We
propose a modification of the post-processing phase that uses high-scoring
object detections from nearby frames to boost scores of weaker detections
within the same clip. We show that our method obtains superior results to
state-of-the-art single image object detection techniques. Our method placed
3rd in the video object detection (VID) task of the ImageNet Large Scale Visual
Recognition Challenge 2015 (ILSVRC2015).
