In order to advance the field of computer vision in the direction of “strong AI”, it’s necessary to address the subproblems of creating a system that can “see” in a way comparable to a human or animal. Due to very recent advances in depth-sensing imaging technology, it is now possible to generate accurate and detailed depth maps that can be used for image segmentation, mapping, and other higher-level processing functions needed for these subproblems. Using this technology, I describe a method for identifying a moving object in video and segmenting the image of the object based on its motion. This creates a coarse vector field where each segment denotes a region of the object that is moving in the same general direction, rounded to the nearest 45 degrees. The approach described combines a conventional background subtraction algorithm, depth sensor data, and a biologically-inspired artificial neural circuit. In most cases the entire process can execute in near real time as the video is captured and is reasonably accurate
