We introduce a detection framework for dense crowd counting and eliminate the
need for the prevalent density regression paradigm. Typical counting models
predict crowd density for an image as opposed to detecting every person. These
regression methods, in general, fail to localize persons accurate enough for
most applications other than counting. Hence, we adopt an architecture that
locates every person in the crowd, sizes the spotted heads with bounding box
and then counts them. Compared to normal object or face detectors, there exist
certain unique challenges in designing such a detection system. Some of them
are direct consequences of the huge diversity in dense crowds along with the
need to predict boxes contiguously. We solve these issues and develop our
LSC-CNN model, which can reliably detect heads of people across sparse to dense
crowds. LSC-CNN employs a multi-column architecture with top-down feedback
processing to better resolve persons and produce refined predictions at
multiple resolutions. Interestingly, the proposed training regime requires only
point head annotation, but can estimate approximate size information of heads.
We show that LSC-CNN not only has superior localization than existing density
regressors, but outperforms in counting as well. The code for our approach is
available at this https URL

介绍了一种用于人群计数的检测框架，采用 LSC-CNN 模型，解决了人群中的人头检测、计数等问题。