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 multiscale semantic refining 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.

中文翻译：

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SSR-HEF：通过多尺度语义细化和困难示例聚焦进行人群计数


基于密度图的人群计数通常被视为回归任务。深度学习用于学习图像内容与人群密度分布之间的映射。尽管取得了巨大的成功，但一些远离摄像头的行人很难被检测到。而且难例的数量往往更多。现有的简单欧几里德距离算法的方法不加区别地优化困难示例和简单示例，使得困难示例的密度通常被错误地预测为较低甚至为零，从而导致较大的计数误差。为了解决这个问题，我们首先提出了用于人群计数回归任务的硬样本聚焦（HEF）算法。 HEF 算法通过削弱简单示例的贡献，使我们的模型快速关注困难示例。然后，对于错误估计的困难示例将给予更高的重视。此外，人群场景中的尺度变化很大，尺度标注是劳动密集型且昂贵的。通过提出多尺度语义细化策略，我们模型的较低层可以突破深度学习的限制，捕获不同尺度的语义特征，以充分处理尺度变化。我们对六个基准数据集进行了广泛的实验来验证所提出的方法。结果表明我们提出的方法优于最先进的方法。此外，我们设计的模型更小、速度更快。