基于多尺度特征融合的人群密度检测

基于深度学习的人群密度检测算法取得了巨大进步,但该算法在实际复杂场景中的检测准确性和鲁棒性还有很大的提升空间.复杂场景下目标尺度不一致和背景信息干扰等因素使得人群密度检测成为一项具有挑战性的任务.针对该问题,提出了一种基于多尺度特征融合的人群密度检测网络.该网络首先利用不同分辨率图像并行交互提取人群粗细粒度特征,并引入多层次特征融合机制,以充分利用多层尺度信息.其次采用空间和通道注意力机制突出人群特征权重,聚焦感兴趣的人群,降低背景信息干扰,生成高质量密度图.实验结果表明,在多个典型的公共数据集上与具有代表性的人群密度检测方法相比,多尺度特征融合的人群密度检测网络具有良好的准确性和鲁棒性.

Crowd Density Detection Based on Multi-scale Feature Fusion

The crowd density detection algorithm based on deep learning has made great progress, while there is still a lot of room for improvement in the detection accuracy and robustness of the algorithm in actual complex scenes. Factors such as inconsistent object scales and background information interference in complex scenes make crowd density detection a challenging task. Aiming at this problem, this study proposes a crowd density detection network based on multi-scale feature fusion. The network first uses images of different resolutions to interactively extract coarse and fine-grained features of the crowd and introduces a multi-level feature fusion mechanism to make full use of multi-level scale information. Secondly, the study utilizes the spatial and channel attention mechanism to highlight the weight of crowd characteristics, focus on interested crowds, reduce background information interference, and generate high-quality density maps. Experimental results show that the crowd density detection network with multi-scale feature fusion has better accuracy and robustness than representative crowd density detection methods on multiple typical public datasets.