  Crowd counting based on density maps is generally regarded as a regression
task.Deep learning is used to learn the mapping between image content and crowd
density distribution. Although great success has been achieved, some
pedestrians far away from the camera are difficult to be detected. And the
number of hard examples is often larger. Existing methods with simple Euclidean
distance algorithm indiscriminately optimize the hard and easy examples so that
the densities of hard examples are usually incorrectly predicted to be lower or
even zero, which results in large counting errors. To address this problem, we
are the first to propose the Hard Example Focusing(HEF) algorithm for the
regression task of crowd counting. The HEF algorithm makes our model rapidly
focus on hard examples by attenuating the contribution of easy examples.Then
higher importance will be given to the hard examples with wrong estimations.
Moreover, the scale variations in crowd scenes are large, and the scale
annotations are labor-intensive and expensive. By proposing a multi-Scale
Semantic Refining (SSR) strategy, lower layers of our model can break through
the limitation of deep learning to capture semantic features of different
scales to sufficiently deal with the scale variation. We perform extensive
experiments on six benchmark datasets to verify the proposed method. Results
indicate the superiority of our proposed method over the state-of-the-art
methods. Moreover, our designed model is smaller and faster.
