Head detection-based crowd counting is of great importance and serves as a preprocessing step in many visual applications, for example, counting, tracking, and crowd dynamics understanding. Despite significant importance, limited amount of work is reported in the literature to detect human heads in high-density crowds. The problem of detecting heads in crowded scenes is challenging due to significant scale variations in the scene. In this paper, we tackle this problem by exploiting contextual constraints offer by the crowded scenes. For this purpose, we propose two networks, i.e., sparse-scale convolutional neural network (SS-CNN) and dense-scale convolutional neural network (DS-CNN). SS-CNN detects human heads with coarse information about the scales in the image. DS-CNN utilizes detection obtained from SS-CNN and generates dense scalemap by globally reasoning the coarse scales of detections obtained from SS-CNN via Markov Random Field (MRF). The dense scalemap has unique property that it captures all scale variations in image and provides an aid in generating scale-aware proposals. We evaluated our framework on three challenging state-of-the-art datasets, i.e., UCF-QNRF, WorldExpo’10, and UCF_CC_50. Experiment results show that proposed framework outperforms existing state-of-the-art methods.

基于头部检测的人群计数非常重要，它是许多视觉应用程序中的预处理步骤，例如计数，跟踪和了解人群动态。尽管非常重要，但文献中报道的工作量有限，无法检测出高密度人群中的人头。由于场景中比例的显着变化，在拥挤的场景中检测头部的问题具有挑战性。在本文中，我们通过利用拥挤场景提供的上下文约束来解决此问题。为此，我们提出了两个网络，即稀疏卷积神经网络（SS-CNN）和密集规模卷积神经网络（DS-CNN）。SS-CNN用关于图像比例的粗略信息来检测人的头部。DS-CNN利用从SS-CNN获得的检测结果，并通过对通过Markov随机场（MRF）从SS-CNN获得的检测结果的总体比例进行全局推理来生成密集比例图。密集比例尺具有独特的属性，可以捕获图像中所有比例尺的变化，并有助于生成比例尺建议。我们在三个具有挑战性的最新数据集（即UCF-QNRF，WorldExpo'10和UCF_CC_50）上评估了我们的框架。实验结果表明，提出的框架优于现有的最新方法。，UCF-QNRF，WorldExpo'10和UCF_CC_50。实验结果表明，提出的框架优于现有的最新方法。，UCF-QNRF，WorldExpo'10和UCF_CC_50。实验结果表明，提出的框架优于现有的最新方法。