  As humans we possess an intuitive ability for navigation which we master
through years of practice; however existing approaches to model this trait for
diverse tasks including monitoring pedestrian flow and detecting abnormal
events have been limited by using a variety of hand-crafted features. Recent
research in the area of deep-learning has demonstrated the power of learning
features directly from the data; and related research in recurrent neural
networks has shown exemplary results in sequence-to-sequence problems such as
neural machine translation and neural image caption generation. Motivated by
these approaches, we propose a novel method to predict the future motion of a
pedestrian given a short history of their, and their neighbours, past
behaviour. The novelty of the proposed method is the combined attention model
which utilises both "soft attention" as well as "hard-wired" attention in order
to map the trajectory information from the local neighbourhood to the future
positions of the pedestrian of interest. We illustrate how a simple
approximation of attention weights (i.e hard-wired) can be merged together with
soft attention weights in order to make our model applicable for challenging
real world scenarios with hundreds of neighbours. The navigational capability
of the proposed method is tested on two challenging publicly available
surveillance databases where our model outperforms the current-state-of-the-art
methods. Additionally, we illustrate how the proposed architecture can be
directly applied for the task of abnormal event detection without handcrafting
the features.
