  Consecutive frames in a video are highly redundant. Therefore, to perform the
task of video object detection, executing single frame detectors on every frame
without reusing any information is quite wasteful. It is with this idea in mind
that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to
video object detection. Our contributions are twofold. First, we propose a new
architecture that allows the usage of information from nearby frames to enhance
feature maps. Second, we propose a novel module to merge feature maps of same
dimensions using re-ordering of channels and 1 x 1 convolutions. We then
demonstrate that RN-VID achieves better mean average precision (mAP) than
corresponding single frame detectors with little additional cost during
inference.
