The location of the body parts is done in a special range, starting with the most stable part. In most applications, this is the head. This field describes the motion in the image plane. It should not be too smooth, because different body parts move into different directions, as for example the arm and the trunk. Suitable features extracted from the field are for example the mean or direction in several regions, or moments.
Classification Having extracted the feature vectors, the classification itself is done.
Learning algorithms are used for this item, as gait of different people vary a lot. It is possible to recognize someone by his gait, and even if the same person is not walking in exactly the same way every time. It is also used in gesture recognition and in sign language. Gait is modeled as a sequence of states , each state is represented by an output probability which is the probability of observing the feature vector in state . The topology of the HMM should consider the fact that gait is a periodic action, one gait cycle contains a left and a right step.