  Computer vision techniques have been used to produce accurate and generic
crowd count estimators in recent years. Due to severe occlusions, appearance
variations, perspective distortions and illumination conditions, crowd counting
is a very challenging task. To this end, we propose a deep spatial regression
model(DSRM) for counting the number of individuals present in a still image
with arbitrary perspective and arbitrary resolution. Our proposed model is
based on Convolutional Neural Network (CNN) and long short term memory (LSTM).
First, we put the images into a pretrained CNN to extract a set of high-level
features. Then the features in adjacent regions are used to regress the local
counts with a LSTM structure which takes the spatial information into
consideration. The final global count is obtained by a sum of the local
patches. We apply our framework on several challenging crowd counting datasets,
and the experiment results illustrate that our method on the crowd counting and
density estimation problem outperforms state-of-the-art methods in terms of
reliability and effectiveness.
