Detecting cats visually by means of anomaly detection
You could simplify your problem significantly by using a motion/change detection approach. For example, you could compare each image/frame with one from an early time (e.g., a minute earlier), then only consider pixels that have changed since the earlier time. You could then extract the rectangular region of change and use that as the basis for your classification or anomaly detection.

Taking this type of approach can significantly simplify your classifier and reduce your false target rate because you can ignore anything that is not roughly the size of a cat (e.g., a person or bird). You would then use the extracted change regions that were not filtered out to form the training set for your classifier (or anomaly detector).

Just be sure to get your false target rate sufficiently low before mounting a laser turret to your feline intrusion detection system.