  Recent works on crowd counting mainly leverage CNNs to count by regressing
density maps, and have achieved great progress. In the density map, each person
is represented by a Gaussian blob, and the final count is obtained from the
integration of the whole map. However, it is difficult to accurately predict
the density map on dense regions. A major issue is that the density map on
dense regions usually accumulates density values from a number of nearby
Gaussian blobs, yielding different large density values on a small set of
pixels. This makes the density map present variant patterns with significant
pattern shifts and brings a long-tailed distribution of pixel-wise density
values. We propose a simple and effective Learning to Scale (L2S) module, which
automatically scales dense regions into reasonable closeness levels (reflecting
image-plane distance between neighboring people). L2S directly normalizes the
closeness in different patches such that it dynamically separates the
overlapped blobs, decomposes the accumulated values in the ground-truth density
map, and thus alleviates the pattern shifts and long-tailed distribution of
density values. This helps the model to better learn the density map. We also
explore the effectiveness of L2S in localizing people by finding the local
minima of the quantized distance (w.r.t. person location map). To the best of
our knowledge, such a localization method is also novel in localization-based
crowd counting. We further introduce a customized dynamic cross-entropy loss,
significantly improving the localization-based model optimization. Extensive
experiments demonstrate that the proposed framework termed AutoScale improves
upon some state-of-the-art methods in both regression and localization
benchmarks on three crowded datasets and achieves very competitive performance
on two sparse datasets.
