  For crowded scenes, the accuracy of object-based computer vision methods
declines when the images are low-resolution and objects have severe occlusions.
Taking counting methods for example, almost all the recent state-of-the-art
counting methods bypass explicit detection and adopt regression-based methods
to directly count the objects of interest. Among regression-based methods,
density map estimation, where the number of objects inside a subregion is the
integral of the density map over that subregion, is especially promising
because it preserves spatial information, which makes it useful for both
counting and localization (detection and tracking). With the power of deep
convolutional neural networks (CNNs) the counting performance has improved
steadily. The goal of this paper is to evaluate density maps generated by
density estimation methods on a variety of crowd analysis tasks, including
counting, detection, and tracking. Most existing CNN methods produce density
maps with resolution that is smaller than the original images, due to the
downsample strides in the convolution/pooling operations. To produce an
original-resolution density map, we also evaluate a classical CNN that uses a
sliding window regressor to predict the density for every pixel in the image.
We also consider a fully convolutional (FCNN) adaptation, with skip connections
from lower convolutional layers to compensate for loss in spatial information
during upsampling. In our experiments, we found that the lower-resolution
density maps sometimes have better counting performance. In contrast, the
original-resolution density maps improved localization tasks, such as detection
and tracking, compared to bilinear upsampling the lower-resolution density
maps. Finally, we also propose several metrics for measuring the quality of a
density map, and relate them to experiment results on counting and
localization.
