  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.
