Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.

通过深度学习和优化方法，本文探讨了从低分辨率、高遮挡和大透视的网络摄像头视频中理解交通密度的问题，并提出了两种方法：一种是基于排名约束回归的方法，另一种是基于全卷积网络的方法。在一个大规模的交通视频数据集上进行了广泛的评估和比较。其中全卷积网络的方法相对于现有的基线取得了显著的改善。收集和标记的大规模交通视频数据集可以解决现有数据集所面临的限制。