This system measured optical flow and tracked vehicles by classifying their headlights, bonnet, front window, and roof area.
In [17], Kitt et al. use a sparse set of static image features (e.g., corners) with measured optical flow and disparity and apply the Longuet-Higgins-Equations with an implicit extended Kalman filter to recover the ego-motion.
which can be rewritten to include quality measure of the optical flow.
where the first term is the OF constraint, the second is a measure of the Optical Flow field smoothness, and is a weighting factor that controls the influence of the smoothness constraint.
Since the beginnings of computer vision, a diversity of tracking methods have been proposed, some of them construct path and state evolution estimations using a Bayesian framework (e.g., particle filters, hidden Markov models), others measure the perceived optical flow in order to determine object displacements and scale changes (median flow) [1].
We go beyond this work by proposing a multi modal similarity measure, composed of phase, colour and optical flow measurement, and combine it with a classical good continuation criterion forming a novel multi modal definition of the affinity between primitives.
For the movement dissimilarity, we use the one based on optical flow measure.
In the temporal attention model, we propose a novel histogram of average optical flow to measure the motion contrast of the different pixels.
A methodology called motion magnification has been developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements.
There are some vision techniques used to accurately measure the amount of motion, such as optical flow and feature tracking.
