Real-Time Physical Distancing Monitor

With the OpenCV AI Kit (OAK) Device

BadgeWe are one
of the
winning teams
of Phase 1 in the
Competition 2021


Real-Time Physical Distancing Monitor


“Andean Horizon”


South America + Central America + Caribbeans



Team Type

General Team

Team Members

Sole team member: Raul Alvarez-Torrico

Bolivian citizen, he has a BEng in electronics engineering and has also successfully completed more than 15 MOOCs in robotics, computer vision and artificial intelligence. In his spare time, he likes to experiment with robotics, artificial intelligence, embedded systems and IoT.

Published Projects:


The Real-Time Physical Distancing Monitor is a system for the purpose of monitoring and evaluating physical distancing (known also as social distancing) policy compliance in open public areas. The system comprises one or more Detection Nodes and a Cloud Server. Each Detection Node comprises an OAK-D camera and an embedded computer to detect pedestrians and compute real distances between them. These data are then uploaded to a Cloud Server and stored in a database for further analysis and visualization. The Cloud Server presents a home page in which a map of the city can be seen with markers in all places where there is a Detection Node installed. System users can access the home page and click any marker to browse to the corresponding node's monitoring page, in which graphical data about physical distancing in the current location is seen.

These graphical data comprise a timeline plot of the total number of pedestrians detected by the system, the total number of physical distancing violations among the detected pedestrians and the violations as a percentage of all possible one-to-one physical interactions between them. The system is meant to be used by authorities in charge of enforcing physical distancing policies in order to evaluate, correct and re-issue better policies. It is meant to be used as well by regular citizens to access real-time data about how crowded is a given public spot. This gives them insight about the risk degree of being infected by the virus in that place, due to the degree of physical distancing violations. Because the monitoring page can show historical data as well, it is possible to evaluate over-the-time the place's behavior regarding physical distancing. For instance, to determine which days and which hours of the day the physical distancing violation index is higher, with consequently higher risk degree of infection. With this information at hand, the authorities can devise alternatives to reduce crowds in peak hours, or the citizens can voluntarily avoid certain places and/or time of the day with higher infection risk.

To protect the privacy of the individuals, the system blurs the detected pedestrians in the image and a low-resolution copy of it is sent to the Cloud Server for visualization purposes. No other images or data regarding the individuals detected in the image are stored permanently in the Detection Nodes or in the Cloud Server.

Block Diagram

Block Diagram

Cloud Server Main Page

Cloud Server Main Page

Individual Node Monitoring Page

Individual Node Monitoring Page

The OAK-D Device

OAK-D         OAK-D

1200 OAK-D devices will be given to phase one winners to help them complete their phase two projects. The OAK-D is a variant of the OpenCV AI Kit (OAK) capable of Spatial AI.

What is Spatial AI?

It’s the capability for AI to be applied to the physical world – to tell you what an object is and where it is in 3D space – in real time.

It does this by running object detection off of its integrated 12MP RGB camera and combining the results with its integrated stereo-depth engine. You can run a variety of deep learning models support by OpenVINO and OAK-D automatically augments them with spatial data from the integrated stereo depth engine.

Project Log

Thu 3/4/2021