Project Abstract

Many City agencies are involved in the use, planning, and design of public space but good data on pedestrian flows can be hard to come by. Manual counts take coordination and planning as well as staff costs. Computer-vision (CV) counting technologies are being tested in the city now but it is already clear that the infrastructure requirements (tapping into electricity and mounting to light poles) will be a limiting factor in using this technology more broadly and particularly for shorter-term studies where the infrastructure investment is not worth the time and effort. A low-cost, battery-powered CV sensor can help fill the data gap and allow agencies to utilize privacy-protected automated counts in short-term deployments with minimal infrastructure requirements.

Project Description & Overview

In recent years, many hardware manufacturers have created development boards that support low-power computer vision (LPCV) applications. In addition, there has also been a fair amount of research done within academia to create low-power models for LPCV. This proposal aims to take advantage of recent technology advances to develop a hardware device that can be battery operated and utilized by New York City agencies to count pedestrians as they move through public space in the city. As an added resource to the proposed R&D, partnering with a technology developer as a development partner is a possibility.

In terms of requirements, the device should aim to work in outdoor environments, run off a battery for 2-4 weeks (either standalone or with PV), connect to the cloud via LoRaWAN or cellular, and be able to detect at least one object type at a time, e.g. pedestrian or cyclist).

Datasets

This project is design to create new datasets.

Competencies

Hardware engineering, AI/ML/CV development, data visualization

Learning Outcomes & Deliverables

An understanding of how to achieve useful computer vision applications using low-power electronics and visualizing count data contextually in ways that make it relevant for the agency use case.

Students

Abdulaziz Alaql, Turbold Baatarchuluu, Alec Bardey, Branden DuPont