Learning Flexible Models from Image Sequences

The “Point Distribution Model”, derived by analysing the modes of variation of a set of training examples, can be a useful tool in machine vision. One of the drawbacks of this approach to date is that the training data is acquired with human intervention where fixed points must be selected by eye from example images. A method is described for generating a similar flexible shape model automatically from real image data. A cubic B-spline is used as the shape vector for training the model. Large training sets are used to generate a robust model of the human profile for use in the labelling and tracking of pedestrians in real-world scenes. Furthermore, an extended model is described which incorporates direction of motion, allowing the extrapolation of direction from shape.