Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront

通过深度学习评估公共开放空间的利用率：以底特律河岸开放空间研究为例

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 en- vironments 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 ap- pealing to make use of surveillance cameras and to extract user-related information through computer vision.

This pa- per proposes a proof-of-concept deep learning computer vision framework for measuring human activities quanti- tatively in POS and demonstrates a case study of the pro- posed framework using the Detroit Riverfront Conservancy (DRFC) surveillance camera network.

A custom image dataset is presented to train the framework; the dataset in- cludes 7826 fully annotated images collected from 18 cam- eras across the DRFC park space under various illumina- tion conditions. Dataset analysis is also provided as well as a baseline model for one-step user localization and activ- ity recognition. The mAP results are 77.5% for pedestrian detection and 81.6% for cyclist detection. Behavioral maps are autonomously generated by the framework to locate dif- ferent POS users and the average error for behavioral lo- calization is within 10 cm.

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