  Video coding technology has been continuously improved for higher compression
ratio with higher resolution. However, the state-of-the-art video coding
standards, such as H.265/HEVC and Versatile Video Coding, are still designed
with the assumption the compressed video will be watched by humans. With the
tremendous advance and maturation of deep neural networks in solving computer
vision tasks, more and more videos are directly analyzed by deep neural
networks without humans' involvement. Such a conventional design for video
coding standard is not optimal when the compressed video is used by computer
vision applications. While the human visual system is consistently sensitive to
the content with high contrast, the impact of pixels on computer vision
algorithms is driven by specific computer vision tasks. In this paper, we
explore and summarize recent progress on computer vision task oriented video
coding and emerging video coding standard, Video Coding for Machines.
