Abstract. Crowd density estimation is an important part of intelligent crowd monitoring. However, there are still many problems in density estimation due to the complexity of crowd scenes. Aiming at the high-density scenes with varied scales, we present a method based on cross-adversarial loss and global feature for crowd counting, so as to achieve the purpose of capturing more feature details and reducing the impact of background noise more effectively. First, we use the cross-adversarial loss to generate the residual map, which makes use of the consistency between different scales and solves the homogenization problem of fused density map. Then, we extract large-range context information and focus on key information in global spatial features for the generation of a residual map. Finally, the high-resolution density map is used to estimate the crowd counting. Experiments on three datasets confirm that the proposed method has good adaptability in scenes with obvious distribution change, not just in extracting high-quality features for density map estimation but also for accurate crowd counting.

中文翻译：

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使用交叉对抗损失和全局特征的人群计数

摘要。人群密度估计是智能人群监控的重要组成部分。然而，由于人群场景的复杂性，密度估计仍然存在很多问题。针对不同尺度的高密度场景，我们提出了一种基于交叉对抗损失和全局特征的人群计数方法，以达到捕捉更多特征细节和更有效降低背景噪声影响的目的。首先，我们使用cross-adversarial loss生成残差图，利用不同尺度之间的一致性，解决了融合密度图的同质化问题。然后，我们提取大范围上下文信息并关注全局空间特征中的关键信息以生成残差图。最后，高分辨率密度图用于估计人群计数。在三个数据集上的实验证实，所提出的方法在分布变化明显的场景中具有良好的适应性，不仅可以提取用于密度图估计的高质量特征，而且可以用于准确的人群计数。