22 research outputs found
Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd
In this paper, we propose a novel map for dense crowd localization and crowd
counting. Most crowd counting methods utilize convolution neural networks (CNN)
to regress a density map, achieving significant progress recently. However,
these regression-based methods are often unable to provide a precise location
for each person, attributed to two crucial reasons: 1) the density map consists
of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense
region of the density map. To tackle this issue, we propose a novel Focal
Inverse Distance Transform (FIDT) map for crowd localization and counting.
Compared with the density maps, the FIDT maps accurately describe the people's
location, without overlap between nearby heads in dense regions. We
simultaneously implement crowd localization and counting by regressing the FIDT
map. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art localization-based methods in crowd localization tasks,
achieving very competitive performance compared with the regression-based
methods in counting tasks. In addition, the proposed method presents strong
robustness for the negative samples and extremely dense scenes, which further
verifies the effectiveness of the FIDT map. The code and models are available
at https://github.com/dk-liang/FIDTM.Comment: The code and models are available at
https://github.com/dk-liang/FIDT
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Due to domain shift, a large performance drop is usually observed when a
trained crowd counting model is deployed in the wild. While existing
domain-adaptive crowd counting methods achieve promising results, they
typically regard each crowd image as a whole and reduce domain discrepancies in
a holistic manner, thus limiting further improvement of domain adaptation
performance. To this end, we propose to untangle \emph{domain-invariant} crowd
and \emph{domain-specific} background from crowd images and design a
fine-grained domain adaption method for crowd counting. Specifically, to
disentangle crowd from background, we propose to learn crowd segmentation from
point-level crowd counting annotations in a weakly-supervised manner. Based on
the derived segmentation, we design a crowd-aware domain adaptation mechanism
consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer
(CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide
crowd features transfer across domains beyond background distractions. The CDA
module dedicates to regularising target-domain crowd density generation by its
own crowd density distribution. Our method outperforms previous approaches
consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table