  Precise knowledge about the size of a crowd, its density and flow can provide
valuable information for safety and security applications, event planning,
architectural design and to analyze consumer behavior. Creating a powerful
machine learning model, to employ for such applications requires a large and
highly accurate and reliable dataset. Unfortunately the existing crowd counting
and density estimation benchmark datasets are not only limited in terms of
their size, but also lack annotation, in general too time consuming to
implement. This paper attempts to address this very issue through a content
aware technique, uses combinations of Chan-Vese segmentation algorithm,
two-dimensional Gaussian filter and brute-force nearest neighbor search. The
results shows that by simply replacing the commonly used density map generators
with the proposed method, higher level of accuracy can be achieved using the
existing state of the art models.
