Video-Based vs Laser-Based Tracking of Dynamic Environments

In both fields of motion-capture and surface-capture (scanning) the need to discriminate unique points in space is paramount to the accuracy of the capture system. When I was first conceiving rotoCap I tried to figure out this problem by looking what I had to work with and not having ready access to the specialized cameras of a Vicon system or laser scanner I turned to consumer video cameras. I conceived I could lessen the amount of computer load by not having the program calculate trajectories but color. An infrared machine-vision camera is suited to higher resolutions and frame rates, how could the lowly camcorder compete? The answer is color.

Most machine vision cameras used in mocap and 3d scanning are grayscale in which their output is turned into a binary image. A binary image is simply a high-contrast image that is only black and white with no gradient. This makes the image easier to process for the software since it only deals with white dots on a black background, but the other calculations it uses to discriminate between two white dots which come near to each other can be complex and costly to the performance. By adding in the color value, the need to track each dot is made easier by giving each its own RGB value.

Why bring this up? The DARPA Grand Challenge is a great source for inspiration because of the real-world applications of scanning/mocap. The Stanford University team's Standley uses a hybrid system of laser scanning and a video overlay allowing it to use color to tell the difference between the road color and everything else. This is a great confirmation that we're both on the right track when it comes to using color over binary alone.