C2SMART COVID-19 Data Dashboard - Sociability

To investigate crowd density and the effectiveness of social distancing strategies, C2SMART researchers have introduced a low-cost, AI-driven big data acquisition framework leveraging hundreds of traffic cameras along with a deep learning-based video processing method.

Object detection and distance approximation between pedestrian pairs are applied to traffic camera videos at multiple NYC and Seattle locations to analyze local social distancing patterns. This sociability board shows some examples of the application.

The methology can be found from the white paper issue 3

Additonal reference can be found from the following paper:

Zuo, F., Gao, J., Kurkcu, A., Yang, H., Ozbay, O. and Ma, Q., Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic, Journal of Transport & Health, Volume 21, 2021 Free Link by May 07

For real-time analysis, please contact us for more information.


This application may contain and/or utilize information which was originally compiled by the New York City Department of Transportation (DOT) for governmental purposes; the information may subsequently been modified by entity/entities other than DOT. DOT and the City of New York make no representation as to the accuracy or usefulness of the information provided by this application or the information’s suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. The public is advised to observe posted signage for compliance with applicable laws and regulations.

A glance at crowd density during COVID-19 (Apr-May 2020)

For real-time analysis, please contact jingqin.gao@nyu.edu for more information.

2 Ave and E 14 St

Whitehall and Water St

8 AVE and 23 ST

9 AVE and 42 ST

Main St and Roosevelt Ave

6 AVE and 42 ST

Seattle: Broadway and Pike St