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2020 Enhancing Computer Vision for Public Safety Challenge

Enhancing Computer Vision for Public Safety Challenge logo

The Enhancing Computer Vision for Public Safety Challenge is an open innovation competition from NIST PSCR focused on advancing the capacity of no-reference (NR) metrics and computer vision algorithms to support public safety missions. During this 2-phase challenge, NIST PSCR will award up to $240,000. 

PSCR seeks solvers to create image or video datasets that depict camera capture problems, such as grime on the lens or sun flare, that cause issues for computer vision applications. These training datasets will help further image quality research and development aimed at better diagnosing and predicting camera problems. This supports PSCR’s mission by expanding the research community and raising awareness of the public safety use case and NIST’s research in this area.

Phase 2 Winners Announced!

Congratulation to the following contestants selected as final winners of the NIST Enhancing Computer Vision for Public Safety Challenge! The Phase 2 Winners are:

 Phase 2 Awards: $6,000


The University of Texas at Austin Laboratory for Image & Video Engineering logo


Team CalAster logo

Dr. Burak Ozer


IUPUI Center for Visual Info Sensing and Computing logo

Team iAI Tech-NJIT

Innovative AI Technology and New Jersey Institute of Technology logos



CDVL Distribution Prize: $12,000

Team iAI Tech-NJIT






Best in Class Prize: $5,000

Best Dataset: Team iAI Tech-NJIT

Best Assessment Data: Team CalAster


Challenge Background

A roadblock to the deployment of computer vision and video analytics is the myriad problems cameras experience when deployed in real world environments. PSCR wants to stimulate research in the unique area of image quality assessment to support public safety. 

To accomplish this, new training datasets and innovative algorithms – referred to as a NR metric for image quality assessment (IQA) or video quality assessment (VQA) – are needed to enhance computer visions systems’ ability to predict and resolve image quality problems in first responder scenarios. Utilizing the NR metrics, deployed systems can implement complex strategies to solve these image quality problems before they even reach public safety analytics.

Challenge Outcome

At the contestant’s discretion, these images and videos will be freely distributed via NTIA’s Consumer Digital Video Library (, providing the research community with valuable datasets for R&D. In the future, successful challenge solutions will be able to identify camera quality problems so that analytics researchers can use these computer resources instead of trying to diagnose and predict these problems manually. The winning solution would also eliminate roadblocks preventing the transition from the development to deployment of new public safety analytics features.

How to Participate

The Computer Vision Challenge is no longer open for submissions.


Review the official rules at for more information.


Created July 14, 2020, Updated December 30, 2022