What kind of feature vector is better to detect whether there is a car in a car park slot ?

Question

My aim is to detect whether a car slot is empty or occupied by a car. Finally, the number of cars will be counted in a car park.

The camera is monitoring the car park as it is seen in the sample pictures. Each car park slot is presented with very less pixels. I select four pixel points to define ROI, and I apply the perspective transformation in the image, please see Image 1.

SVM would be a nice approach to classify the samples and train. Unfortunately, I am not sure about the feature vector.

The challenges: -Shadow of the cars in the adjacent slots -A car is one slot is visible partially in another slot.
-Shadow of the big buildings -Weather changes (sunny, cloudy etc. ) -After the rain, slot color is changed (dry or wet) -Different slots and perspective changes

What kind of features or feature vectors would be the best for the classification?

Thank you in advance,

Answer 1

A color histogram could already be enough if you have enough training data. You can train with shadowed, partly shadowed, non-shadowed empty spots as well as with different cars. It might be difficult to get enough training data, you could also use synthetic data (render cars and shadows on the images).

So it is not only a question about features, but also about training samples.