论文标题
通过深度学习测量公共开放空间的利用：底特律河滨的基准研究
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront
论文作者
论文摘要
体育活动和社交互动是确保健康生活方式的重要活动。公共开放空间（POS），例如公园，广场和绿道，是鼓励这些活动的关键环境。为了评估POS，有必要研究人类如何使用其中的设施。但是，研究使用POS的传统方法是手动的，因此时间和劳动密集型。他们还可以提供定性的见解。使用监视摄像机并通过计算机视觉提取与用户相关的信息是很有吸引力的。本文提出了一个概念验证的深度学习计算机视觉框架，用于在POS中定量测量人类活动，并使用底特律Riverfront Conservancy（DRFC）监视摄像机网络对拟议框架进行了案例研究。提出了自定义图像数据集来训练框架；该数据集包括在各种照明条件下从DRFC公园空间中的18个相机收集的7826个完全注释的图像。还提供了数据集分析以及一步用户本地化和活动识别的基线模型。对于{\ it行人}检测，地图结果为77.5 \％，{\ it Cyclist}检测为81.6 \％。框架自主生成的行为图以定位不同的POS用户，行为定位的平均错误在10 cm之内。
Physical activities and social interactions are essential activities that ensure a healthy lifestyle. Public open spaces (POS), such as parks, plazas and greenways, are key environments that encourage those activities. To evaluate a POS, there is a need to study how humans use the facilities within it. However, traditional approaches to studying use of POS are manual and therefore time and labor intensive. They also may only provide qualitative insights. It is appealing to make use of surveillance cameras and to extract user-related information through computer vision. This paper proposes a proof-of-concept deep learning computer vision framework for measuring human activities quantitatively in POS and demonstrates a case study of the proposed framework using the Detroit Riverfront Conservancy (DRFC) surveillance camera network. A custom image dataset is presented to train the framework; the dataset includes 7826 fully annotated images collected from 18 cameras across the DRFC park space under various illumination conditions. Dataset analysis is also provided as well as a baseline model for one-step user localization and activity recognition. The mAP results are 77.5\% for {\it pedestrian} detection and 81.6\% for {\it cyclist} detection. Behavioral maps are autonomously generated by the framework to locate different POS users and the average error for behavioral localization is within 10 cm.
