  In the field of crowd counting, the current mainstream CNN-based regression
methods simply extract the density information of pedestrians without finding
the position of each person. This makes the output of the network often found
to contain incorrect responses, which may erroneously estimate the total number
and not conducive to the interpretation of the algorithm. To this end, we
propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has
three innovation points. i) A two-branch architecture is used to estimate the
density information and location information separately. ii) Attention
mechanism is used to facilitate feature extraction, which can reduce false
responses. iii) A new density map generation method combining geometric
adaptation and Voronoi split is introduced. Our method can integrate the
pedestrian's head and body information to enhance the feature expression
ability of the density map. Extensive experiments performed on two public
datasets show that our method achieves a lower crowd counting error compared to
other state-of-the-art methods.
