Vision can be surprisingly difficult, especially as you try to tolerate varying conditions. A few good things to research include Blob Finding (searching for contiguous pixels matching certain criteria, usually a threshold of brightness), Image Segmentation (can you have multiple balls in an image?) and general theory on Hue (most vision algorithms work with grayscale or binary images, so you'll first need to transform the image in a way that highlights the orangeness as the criteria for selection.)
Since you are presumably tracking these objects in real time as you move toward them, you might also be interested in learning about tracking models, such as the Kalman filter. It's overkill for what you're doing, but it's interesting and the basic ideas are helpful. Since you presumably know that the object should not be moving very quickly, you can use that fact to filter out false positives that could otherwise lead you to move away from the object. You can put together a simpler version of this kind of filtering by simply ignoring frames that have moved too far from the previous accepted frame (with a few boundary conditions to avoid getting stuck ignoring the object.)