  Crowd counting is a fundamental yet challenging task, which desires rich
information to generate pixel-wise crowd density maps. However, most previous
methods only used the limited information of RGB images and cannot well
discover potential pedestrians in unconstrained scenarios. In this work, we
find that incorporating optical and thermal information can greatly help to
recognize pedestrians. To promote future researches in this field, we introduce
a large-scale RGBT Crowd Counting (RGBT-CC) benchmark, which contains 2,030
pairs of RGB-thermal images with 138,389 annotated people. Furthermore, to
facilitate the multimodal crowd counting, we propose a cross-modal
collaborative representation learning framework, which consists of multiple
modality-specific branches, a modality-shared branch, and an Information
Aggregation-Distribution Module (IADM) to capture the complementary information
of different modalities fully. Specifically, our IADM incorporates two
collaborative information transfers to dynamically enhance the modality-shared
and modality-specific representations with a dual information propagation
mechanism. Extensive experiments conducted on the RGBT-CC benchmark demonstrate
the effectiveness of our framework for RGBT crowd counting. Moreover, the
proposed approach is universal for multimodal crowd counting and is also
capable to achieve superior performance on the ShanghaiTechRGBD dataset.
Finally, our source code and benchmark are released at
{\url{http://lingboliu.com/RGBT_Crowd_Counting.html}}.
