  As a fundamental computer vision task, crowd counting plays an important role
in public safety. Currently, deep learning based head detection is a promising
method for crowd counting. However, the highly concerned object detection
networks cannot be well applied to this problem for three reasons: (1) Existing
loss functions fail to address sample imbalance in highly dense and complex
scenes; (2) Canonical object detectors lack spatial coherence in loss
calculation, disregarding the relationship between object location and
background region; (3) Most of the head detection datasets are only annotated
with the center points, i.e. without bounding boxes. To overcome these issues,
we propose a novel Mask Focal Loss (MFL) based on heatmap via the Gaussian
kernel. MFL provides a unifying framework for the loss functions based on both
heatmap and binary feature map ground truths. Additionally, we introduce
GTA_Head, a synthetic dataset with comprehensive annotations, for evaluation
and comparison. Extensive experimental results demonstrate the superior
performance of our MFL across various detectors and datasets, and it can reduce
MAE and RMSE by up to 47.03% and 61.99%, respectively. Therefore, our work
presents a strong foundation for advancing crowd counting methods based on
density estimation.
