Computer vision also differs from human vision in that it doesn’t “see” like humans do—it reads images as raw pixel data without reference to context or meaning. Where humans intuitively see objects, emotions, and intent, computers need algorithms to define patterns, edges, and movement. We naturally adjust for differences in light, angle, or occlusions, but computer vision generally needs to be programmed further to compensate. Humans also employ depth perception and prior knowledge to make sense of 3D space, while computers typically work on 2D images and need additional techniques like stereo cameras or depth sensors to estimate depth.
So that computers can more easily track what we’re interested in, we use techniques like frame differencing ( movement by detecting differences between frames of video), background subtraction (new objects are highlighted against a static scene), and brightness thresholding (objects are highlighted based on light contrast). More advanced techniques include edge detection, feature tracking, and deep learning algorithms that can detect faces, gestures, or objects. For interactive art, computer vision is frequently utilized by artists to explore themes of control and visibility, as seen in works like Sorting Daemon and Suicide Box.
Sounds good! Maybe you can a generative animation or two in some area of the game.