Motion Estimation Tools¶

Block motion¶

To estimate a block motion field, simply use skvideo.motion.blockMotion

import skvideo.io
import skvideo.motion
import skvideo.datasets

videodata = skvideo.io.vread(skvideo.datasets.bigbuckbunny())

motion = skvideo.motion.blockMotion(videodata)

print(videodata.shape)
print(motion.shape)

Output:

(132, 720, 1280, 3)
(131, 90, 160, 2)

By default, skvideo.motion.blockMotion uses 8x8 pixel macroblocks and the diamond search algorithm.

Block motion compensation¶

Use skvideo.motion.blockComp to use the computed block motion vectors for motion compensation

import skvideo.io
import skvideo.motion
import skvideo.datasets

# compute vectors from bigbuckbunny
videodata = skvideo.io.vread(skvideo.datasets.bigbuckbunny())
motion = skvideo.motion.blockMotion(videodata)

# compensate the video
compmotion = skvideo.motion.blockComp(videodata, motion)

Here is a motion compensated frame from bigbuckbunny

Global motion estimation¶

Typically, estimating the global motion (sometimes referred to as ego-motion) is not as simple as averaging the block motion vectors. Many techniques have been devised to estimate global motion robust against moving foreground objects and noise. The function skvideo.motion.globalEdgeMotion uses edge-based techniques for estimating global motion, with the option to compute error using either the Hausdorff or Hamming distances.