Titan Two color detection vs AI models

Choose a method for the visual question

To count how often a game’s fixed recording icon appears in saved footage, you may be able to compare pixels in the same screen region against a color range. OpenCV documents thresholding in HSV color space as one way to select pixels by hue, saturation and brightness. A similarly colored overlay or change in capture color settings can trigger the same rule.

A trained model can use combinations of shape, color and surrounding features. Its behavior depends on the training examples and the footage it sees in use. Ask which classes the model recognizes and whether the training and test examples cover the game’s current interface and resolution.

Compare both against the same footage

Use clips with and without the visual element. Include bright and dark scenes, menus and changes in interface scaling. Record both missed detections and false alerts. If a rule works only on an uninterrupted sample, check frames near a transition before relying on it for anything else.

ConsoleTuner’s Gtuner IV release notes describe Python and OpenCV integration for computer vision. You can evaluate image analysis with a recorded clip and keep it separate from controller actions. This makes it easier to diagnose whether a result came from the image source or from the analysis itself.

Keep a separate clip for the final comparison

Choose a clip you did not use to tune the color thresholds or select the model. Include one or two known changes in lighting, interface scale or compression. Compare predicted timestamps against your own notes. Save capture settings and label definitions so you can reproduce a false alert later.