Identifying moving objects in a scene, from a sequence of images, for example, a video stream, is a very important, and computation-intensive, endeavor. Generally, known approaches to identifying moving objects in pan imagery use “change detection” where an image with moving objects is differenced with, i.e., compared to, a background image that contains no moving objects. The problem is how to generate a background image from a series of images with many moving objects in close proximity to one another.
There are several different methods of creating a background image. These methods include: a) for a given frame, taking the previous frame as a background; b) averaging n previous frames to form a background; c) morphing the background by making small changes based on the previous image frame; or d) a 3-frame method using the current frame and the two previous frames.
A better method of creating a background image that can be used in change detection processing to identify multiple moving objects in close proximity is needed.