  A significant amount of redundancy exists between consecutive frames of a
video. Object detectors typically produce detections for one image at a time,
without any capabilities for taking advantage of this redundancy. Meanwhile,
many applications for object detection work with videos, including intelligent
transportation systems, advanced driver assistance systems and video
surveillance. Our work aims at taking advantage of the similarity between video
frames to produce better detections. We propose FFAVOD, standing for feature
fusion architecture for video object detection. We first introduce a novel
video object detection architecture that allows a network to share feature maps
between nearby frames. Second, we propose a feature fusion module that learns
to merge feature maps to enhance them. We show that using the proposed
architecture and the fusion module can improve the performance of three base
object detectors on two object detection benchmarks containing sequences of
moving road users. Additionally, to further increase performance, we propose an
improvement to the SpotNet attention module. Using our architecture on the
improved SpotNet detector, we obtain the state-of-the-art performance on the
UA-DETRAC public benchmark as well as on the UAVDT dataset. Code is available
at https://github.com/hu64/FFAVOD.
