The analysis of animate object motion using neural networks and snakes

This paper presents a mechanism for analysing the deformable shape of an object as it moves across the visual field. An object’s outline is detected using active contour models, and is then re-represented as shape, location and rotation invariant axis crossover vectors. These vectors are used as input for a feedforward backpropagation neural network, which provides a confidence value determining how ‘human’ the network considers the given shape to be. The network was trained using simulated human shapes as well as simulated non-human shapes, including dogs, horses and inanimate objects. The network was then tested on unseen objects of these classes, as well as on an unseen object class. Analysis of the network’s confidence values for a given animated object identifies small, individual variations between different objects of the same class, and large variations between object classes. Confidence values for a given object are periodic and parallel the paces being taken by the object

Auto clustering for unsupervised learning of atomic gesture components using minimum description length

We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard EM approach while the determination of the number of components is based on an information criteria, the Minimum Description Length

Learning Behaviour Models of Human Activities

In recent years there has been an increased interest in the modelling and recognition of human activities involving highly structured and semantically rich behaviour such as dance, aerobics, and sign language. A novel approach is presented for automatically acquiring stochastic models of the high-level structure of an activity without the assumption of any prior knowledge. The process involves temporal segmentation into plausible atomic behaviour components and the use of variable length Markov models for the efficient representation of behaviours. Experimental results are presented which demonstrate the generation of realistic sample behaviours and evaluate the performance of models for long-term temporal prediction