  We present a method of estimating the number of people in high density crowds
from still images. The method estimates counts by fusing information from
multiple sources. Most of the existing work on crowd counting deals with very
small crowds (tens of individuals) and use temporal information from videos.
Our method uses only still images to estimate the counts in high density images
(hundreds to thousands of individuals). At this scale, we cannot rely on only
one set of features for count estimation. We, therefore, use multiple sources,
viz. interest points (SIFT), Fourier analysis, wavelet decomposition, GLCM
features and low confidence head detections, to estimate the counts. Each of
these sources gives a separate estimate of the count along with confidences and
other statistical measures which are then combined to obtain the final
estimate. We test our method on an existing dataset of fifty images containing
over 64000 individuals. Further, we added another fifty annotated images of
crowds and tested on the complete dataset of hundred images containing over
87000 individuals. The counts per image range from 81 to 4633. We report the
performance in terms of mean absolute error, which is a measure of accuracy of
the method, and mean normalised absolute error, which is a measure of the
robustness.
