  The crowd counting task aims at estimating the number of people located in an
image or a frame from videos. Existing methods widely adopt density maps as the
training targets to optimize the point-to-point loss. While in testing phase,
we only focus on the differences between the crowd numbers and the global
summation of density maps, which indicate the inconsistency between the
training targets and the evaluation criteria. To solve this problem, we
introduce a new target, named local counting map (LCM), to obtain more accurate
results than density map based approaches. Moreover, we also propose an
adaptive mixture regression framework with three modules in a coarse-to-fine
manner to further improve the precision of the crowd estimation: scale-aware
module (SAM), mixture regression module (MRM) and adaptive soft interval module
(ASIM). Specifically, SAM fully utilizes the context and multi-scale
information from different convolutional features; MRM and ASIM perform more
precise counting regression on local patches of images. Compared with current
methods, the proposed method reports better performances on the typical
datasets. The source code is available at
https://github.com/xiyang1012/Local-Crowd-Counting.
