Learning spatio-temporal patterns for predicting object behavior

摘要

Rule-based systems employed to model complex object behaviours, do not necessarily provide a realistic portrayal of true behaviour. To capture the real characteristics in a specific environment, a better model may be learnt from observation. this paper presents a novel approach to learning long-term spatio-temporal patterns of objects in image sequences, using a neural network paradigm to predict future behavior. The results demonstrate the application of our approach to the problem of predicting animal behavior in response to a predator.