The main aim of specifying the motion type is to facilitate the correct identification of objects from one timepoint to the next. When several objects are moving around in 3D space, it is quite likely that at some point ambiguous situations arise when multiple candidates might be considered as the tracked object. 

In Vision4D, 3 methods of motion detection are available:

• If the objects are generally moving in a straight line then a linear regression algorithm is best. that way, if two objects come into close proximity from differing directions we assume that the one most aligned with the previous movement is most likely to be the same object. Of course this method doesn't perform as well if the objects don't move in a straight line.
• If the objects generally move forward, but sometimes veer in one direction or another then the Conal Angle approach will be better suited. In this case, we define the conal angle and assume that the objects within this angle of previous movements are most likely to be the same as before. However, this may not be sufficient if the objects can move backward as well as forward.
• When the expected direction of motion can change at any moment and the movement is essentially chaotic we can use a Brownian motion algorithm which looks for candidates anywhere within a search radius of a previous position and assumes the closest one is the most likely.

The size of the search radius, in all cases, must be set in line with the expected movement from one timepoint to the next, bearing in mind the need to reduce this distance to a practical range during the acquisition of the images.

When selecting Linear Regression or Conal Angle, additional parameters need to be set.

In both cases we can set the "Max time points" option to define how many time points to use to calculate the direction of movement.

In the case of Conal Angle, we also need to set the maximum permitted deviation from previous directions.