Pattern Analysis and Applications ( IF 3.9 ) Pub Date : 2021-08-20 , DOI: 10.1007/s10044-021-01017-4 Sifan Peng 1 , Baoqun Yin 1 , Xiaoliang Hao 1 , Qianqian Yang 1 , Aakash Kumar 1 , Luyang Wang 1
Crowd counting plays a significant role in crowd monitoring and management, which suffers from various challenges, especially in crowd-scale variations and background interference issues. Therefore, we propose a method named depth and edge auxiliary learning for still image crowd density estimation to cope with crowd-scale variations and background interference problems simultaneously. The proposed multi-task framework contains three sub-tasks including the crowd head edge regression, the crowd density map regression and the relative depth map regression. The crowd head edge regression task outputs distinctive crowd head edge features to distinguish crowd from complex background. The relative depth map regression task perceives crowd-scale variations and outputs multi-scale crowd features. Moreover, we design an efficient fusion strategy to fuse the above information and make the crowd density map regression generate high-quality crowd density maps. Various experiments were conducted on four main-stream datasets to verify the effectiveness and portability of our method. Experimental results indicate that our method can achieve competitive performance compared with other superior approaches. In addition, our proposed method improves the counting accuracy of the baseline network by \(15.6\%\).
中文翻译:
静止图像人群密度估计的深度和边缘辅助学习
人群计数在人群监控和管理中发挥着重要作用,人群监测和管理面临各种挑战,尤其是人群规模变化和背景干扰问题。因此,我们提出了一种名为深度和边缘辅助学习的方法,用于静止图像人群密度估计,以同时应对人群规模变化和背景干扰问题。所提出的多任务框架包含三个子任务,包括人群头部边缘回归、人群密度图回归和相对深度图回归。人群头部边缘回归任务输出独特的人群头部边缘特征,以区分人群与复杂背景。相对深度图回归任务感知人群尺度变化并输出多尺度人群特征。此外,我们设计了一种高效的融合策略来融合上述信息,并使人群密度图回归生成高质量的人群密度图。在四个主流数据集上进行了各种实验,以验证我们方法的有效性和可移植性。实验结果表明,与其他优越的方法相比,我们的方法可以实现有竞争力的性能。此外,我们提出的方法通过以下方式提高了基线网络的计数精度\(15.6\%\)。