  In this paper we advance the state-of-the-art for crowd counting in high
density scenes by further exploring the idea of a fully convolutional crowd
counting model introduced by (Zhang et al., 2016). Producing an accurate and
robust crowd count estimator using computer vision techniques has attracted
significant research interest in recent years. Applications for crowd counting
systems exist in many diverse areas including city planning, retail, and of
course general public safety. Developing a highly generalised counting model
that can be deployed in any surveillance scenario with any camera perspective
is the key objective for research in this area. Techniques developed in the
past have generally performed poorly in highly congested scenes with several
thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced
by the work of (Zhang et al., 2016), consists of the following contributions:
(1) A training set augmentation scheme that minimises redundancy among training
samples to improve model generalisation and overall counting performance; (2) a
deep, single column, fully convolutional network (FCN) architecture; (3) a
multi-scale averaging step during inference. The developed technique can
analyse images of any resolution or aspect ratio and achieves state-of-the-art
counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well
as competitive performance on Shanghaitech Part A.
