Jim Carnicelli's AI Blog

I've done a lot of thinking about this idea of making a program that can characterize the motions of all parts of a video scene. Not surprisingly, I've concluded it's going to be a hard problem. But unlike other cases where I've smacked up against a brick wall, I can see what seems a clear path from here to there. It's just going to take a long time and a lot of steps. Here's an overview of my plan. First, the goal. The most basic purpose is to, as I said above, make a program that can characterize the motions of all parts of a video scene. The program should be able to fill an entire scene with "patches". Each patch will lock onto the content found in that frame and follow it throughout the video or until it can no longer be tracked. So if one patch is planted over the eye of a person walking through the scene, the patch should be able to follow that eye for at least as long as it's visible. Achieving this goal will be valuable because it will pro

Over the weekend, I had one of them epiphany thingies. Sometime last week, I had started up a new vision project involving patch matching. In the past, I've explored this idea with stereo vision and discovering textures. Also, I opined a bit on motion-based segmentation here a couple of years ago. My goal in this new experiment was fairly modest: plant a point of interest (POI) on a video scene and see how well the program can track that POI from frame to frame. I took a snippet of a music video and captured 55 frames into separate JPEG files and made a simple engine with a Sequence class to cache the video frames in memory and a PointOfInterest class, of which the Sequence object would have a list, all busy following POIs. The algorithm for finding the same patch in the next frame is really simple and only involves summing up the red, green, and blue pixel value differences in candidate patches and accepting the candidate with the lowest difference total; trivial, really. When