If we apply the developed local polar edge detection method, or LPED method, to a binary image (with each pixel being either black or white), we can obtain the boundary points of all objects embedded in the more randomly distributed noise background in sub-milli-second time. Then we can apply our newly developed grouping or clustering algorithm to separate the boundary points for all objects into individual-object, boundary-point groups. Then we can apply our fast identification-and-tracking technique to automatically identify each object by its unique geometry shape and track its movement simultaneously for N objects like we did before for two objects. This paper will concentrate at the algorithm design of this superfast grouping technique. It is not like the classical combinatorial clustering algorithm in which the computation time increases exponentially with the number of points to be clustered. It is a linear time grouping method in which the grouping time increases only linearly with the number of the total points to be grouped. The total time for automatic grouping of 100-200 boundary points into separated object boundary groups is about 10 to 50 milli-seconds Live computer experiments will be demonstrated in the presentation.