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2020 Enhancing Computer Vision for Public Safety Challenge

Enhancing Computer Vision for Public Safety logo and an old boat, half of the image is blurry and the other half is clear

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.

 

NIST PSCR Computer Vision Challenge - Final Results
Final recap and results from the NIST PSCR 2020 Computer Vision Prize Challenge.

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

Team LIVE

The University of Texas at Austin LIVE logo

CalAster

Calaster logo

Dr. Burak Ozer

Team IUPUI

This is Team IUPUI's logo for the enhancing computer vision challenge

Team iAI Tech-NJIT

Team iAI Tech-NJIT's logo for the enhancing computer vision challenge

 

 


CDVL Distribution Prize: $12,000

Team iAI Tech-NJIT

CalAster

Team IUPUI

Team LIVE

 

 


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 (CDVL.org), 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.

Questions?

Review the official rules at challenge.gov for more information.

 

Created July 14, 2020, Updated July 16, 2021