Efficient One-stage Video Object Detection by Exploiting Temporal
  Consistency
Guanxiong Sun Yang Hua Guosheng Hu Neil Robertson
Recently, one-stage detectors have achieved competitive accuracy and faster
speed compared with traditional two-stage detectors on image data. However, in
the field of video object detection (VOD), most existing VOD methods are still
based on two-stage detectors. Moreover, directly adapting existing VOD methods
to one-stage detectors introduces unaffordable computational costs. In this
paper, we first analyse the computational bottlenecks of using one-stage
detectors for VOD. Based on the analysis, we present a simple yet efficient
framework to address the computational bottlenecks and achieve efficient
one-stage VOD by exploiting the temporal consistency in video frames.
Specifically, our method consists of a location-prior network to filter out
background regions and a size-prior network to skip unnecessary computations on
low-level feature maps for specific frames. We test our method on various
modern one-stage detectors and conduct extensive experiments on the ImageNet
VID dataset. Excellent experimental results demonstrate the superior
effectiveness, efficiency, and compatibility of our method. The code is
available at https://github.com/guanxiongsun/vfe.pytorch.