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In my opinion it depends on the environment... Maybe you can do a detection on the 3 methods and then use some kind of classification for enforcing the result. This because:

Background subtraction can detect any kind of movement/object
HSV is dependent on light variation
YCbCr is dependent on colour, so light changing and background or objects with the same colour are false positives.

In my opinion it depends on the environment... Maybe you can do a detection on the 3 methods and then use some kind of classification for enforcing the result. This because:

Background subtraction can detect any kind of movement/object
HSV is dependent on light variation
YCbCr is dependent on colour, so light changing and background or objects with the same colour are false positives.

Ok; the part of the classifier is based on a dataset for training. This you can do after you will have some images of "true hand" and some images of "false hands". And it is more linked to the application specifications. (For example you cans see in Figure 1 on the paper of mittal that there is a "false hand on the chest of the second photo", so to remove those kind of detections). Classifiers are based on many types of features, as SIFT; and the evaluation is based on other things, like SVM. But for more info see this and classification in OpenCV like this one, but there are others too.