论文标题
多流网络和人群计数的地面创造
Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
论文作者
论文摘要
由于应用多种应用，法医学，城市规划，监视和安全性，最近引起了人们的关注。在这种情况下，一项具有挑战性的任务被称为人群计数，其主要目的是估计单个图像中存在的人数。在这项工作中开发和评估了一个多流卷积神经网络，该工作接收图像作为输入，并产生一个密度图，该密度图代表了以端到端方式的人的空间分布。为了解决复杂的人群计数问题，例如极度不受限制的规模和观点变化，网络体系结构利用每个流的尺寸过滤器不同的接收场。此外，我们研究了两个最常见的时尚对地面真理产生的影响，并提出了基于微小的面部检测和尺度插值的混合方法。在两个具有挑战性的数据集（UCF-CC-50和上海）上进行的实验表明，使用我们的地面真相生成方法可实现较高的结果。
Crowd scene analysis has received a lot of attention recently due to the wide variety of applications, for instance, forensic science, urban planning, surveillance and security. In this context, a challenging task is known as crowd counting, whose main purpose is to estimate the number of people present in a single image. A Multi-Stream Convolutional Neural Network is developed and evaluated in this work, which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion. In order to address complex crowd counting issues, such as extremely unconstrained scale and perspective changes, the network architecture utilizes receptive fields with different size filters for each stream. In addition, we investigate the influence of the two most common fashions on the generation of ground truths and propose a hybrid method based on tiny face detection and scale interpolation. Experiments conducted on two challenging datasets, UCF-CC-50 and ShanghaiTech, demonstrate that using our ground truth generation methods achieves superior results.
