Detecting cats visually by means of anomaly detection I have a hobby project which I am contemplating committing to as a way of increasing my so far limited experience of machine learning. I have taken and completed the Coursera MOOC on the topic. My question is with regards to the feasibility of the project.

The task is the following:

Neighboring cats are from time to time visiting my garden, which I dislike since they tend to defecate on my lawn. I would like to have a warning system that alerts me when there's a cat present so that I may go chase it off using my super soaker. For simplicity's sake, say that I only care about a cat with black and white coloring.

I have setup a raspberry pi with camera module that can capture video and/or pictures of a part of the garden. 

Sample image:



My first idea was to train a classifier to identify cat or cat-like objects, but after realizing that I will be unable to obtain a large enough number of positive samples, I have abandoned that in favor of anomaly detection.

I estimate that if I captured a photo every second of the day, I would end up with maybe five photos containing cats (out of about 60,000 with sunlight) per day. 

Is this feasible using anomaly detection? If so, what features would you suggest? My ideas so far would be to simply count the number of pixels with that has certain colors; do some kind of blob detection/image segmenting (which I do not know how do to, and would thus like to avoid) and perform the same color analysis on them.
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.