• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

CVPR challenge pushes researchers to improve car accident detection AI

June 21, 2020   Big Data
 CVPR challenge pushes researchers to improve car accident detection AI

AI researchers from more than 30 countries around the world came together this week for the AI City Challenge, a competition to spur the development of better machine learning via tasks such as detecting car accidents and tracking a vehicle across a network of cameras. Now in its fourth year, the challenge pushes AI researchers to create more efficient Intelligent Transportation Systems (ITS).

Teams from Baidu won three of the four competitions: vehicle counting, multi-camera reidentification, and car accident and stalled vehicle detection. Organizing committee member and University of Albany assistant professor Ming-Ching Chang said during the virtual workshop that the top performing model in this category achieved 95.3% accuracy.

A team from Carnegie Mellon University won one of the four challenges for tracking a vehicle over a network of multiple cameras. The benchmark data set for this challenge stretches across 46 camera views spanning 16 intersections in Dubuque, Iowa.

In total, the competition drew more than 800 individual researchers on 300 teams from 36 nations; 76 teams submitted code for final review. The organizing committee included companies like Amazon and Nvidia as well as researchers associated with Iowa State University, Santa Clara University, and the Indian Institute of Technology Kanpur. Organizers called this year’s AI City Challenge the first to use effectiveness and computation efficiency standards the U.S. Department of Transportation says it needs to consider deployment of this form of automation in the wild. Past competitions focused on transportation systems for traffic signaling, public transit, and infrastructure.

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

The AI City Challenge was one of numerous challenges hosted this week at the Computer Vision and Pattern Recognition (CVPR), which was the second-largest annual AI research conference in 2019 and attracted more than 6,500 participants this year. CVPR also hosted competitions for leading a bot through a RoboTHOR simulated environment, as well as the Deepfake Detection Challenge (although The Register reported the winning team was disqualified).

This year at the AI City Challenge workshop, National Institute of Standards and Technology (NIST) officials detailed plans for the 2020 ASAPS Prize Challenge to create a real-time automated system. That competition will focus on building real-time automated analytics systems for law enforcement and first responders to address real-time emergency event detection like a child falling into a harbor, medical emergencies, abandoned building fires, or other situations.

ASAPS is a multimodal challenge for systems capable of ingesting multiple forms of media, from social media posts and text messages to surveillance camera footage and home video doorbells. ASAPS will feature a series of emergency events in a mock 810-acre city over 24 hours, combining simulated data with physically staged emergencies. The competition will also challenge AI researchers to carry out live video analysis rather than applying the AI to a pre-recorded video.

AI City Challenge organizers said next year it may introduce scenarios involving live video analysis. Dash camera video footage is also under consideration.

The addition of a synthetic data set of 190,000 images for the vehicle reidentification challenge was a unique new part of the AI City Challenge this year. Benchmark data sets used in this year’s competition came from footage provided in part by the Iowa Department of Transportation. Nvidia curated data sets used in the challenge.

In related news, opponents of institutional racism demanding lawmakers defund the police in recent weeks have criticized surveillance systems like the kind championed at the AI City Challenge. Earlier this week, New York City lawmakers passed the POST Act requiring the nation’s largest police department to share what surveillance technology it uses.

Let’s block ads! (Why?)

Big Data – VentureBeat

Accident, Challenge, CVPR, Detection, Improve, pushes, researchers
  • Recent Posts

    • PUNNIES
    • Cashierless tech could detect shoplifting, but bias concerns abound
    • Misunderstood Loyalty
    • Pearl with a girl earring
    • Dynamics 365 Monthly Update-January 2021
  • Categories

  • Archives

    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited