Computer Vision System Toolbox

Motion Estimation and Tracking

The system toolbox provides a variety of motion estimation algorithms such as optical flow, block matching, template matching, and background estimation using Gaussian mixture models (GMM). Evaluation metrics for finding the best block match include MSE, MAD, MaxAD, SAD, and SSD. These algorithms create motion vectors, which relate to the whole image, blocks, arbitrary patches, or individual pixels, depending upon the algorithm.

Detecting moving objects using a stationary camera. In this series of video frames, optical flow is calculated and detected motion is shown by overlaying the flow field on top of each frame.

Video tracking is a common use for motion estimation algorithms. You can use calculated motion to identify a moving object or measure the movement of a detected object over consecutive video frames. The system toolbox also provides Kalman filtering to predict the movement of an object in upcoming video frames.

Estimation of camera motion by template matching to calculate a motion vector (left). Using the motion vector, the video is stabilized for camera motion (right).