Biologically-inspired human motion detection
Vijay Laxmi, John N. Carter, R.I. Damper · 2002
A model of motion detection is described, inspired by the capability of humans to recognise biological motion even from minimal information systems such as moving light displays. The model, a feed-forward backpropagation neural network, uses labelled joint data, analogous to light points in such displays. In preliminary work, the model achieves 100 % person classification on a set of 4 artificial subjects and another of 4 real subjects. Subsequently, 100 % motion detection is achieved on a set of 21 subjects. In the latter case, the correspondence problem is also solved by the model, since the network is not ‘told ’ which joint is which. Like human beings, the neural networks perform both tasks within a small fraction of the gait cycle. 1