Skip to main content

Jenoptik helps StarTraq to process Australia driving offences

State of Victoria has rolled out Distracted Driver Camera Project to stop phone use
By Adam Hill April 5, 2023 Read time: 2 mins
Victoria project rolls out distracted driver and seatbelt automated camera enforcement solution across the state (© Flynt | Dreamstime.com)

From this month, motorists in the state of Victoria, Australia, could face a $555 fine and four points on their licence if they are caught using a smartphone or otherwise distracting themselves.

Jenoptik Australia has partnered with StarTraq to process the offences arising from the new rules.

Victoria's Department of Justice and Community Safety (DJCS) has established the Distracted Driver Camera Project which aims to roll out a distracted driver and seatbelt (DDS) automated camera enforcement solution across the state.

The system will be able to detect illegal mobile phone use by drivers and the non-wearing of seatbelts, using ANPR, with number plates of offenders included in incident packages.

Jenoptik uses trailer-mounted camera systems and artificial intelligence to detect offenders and will utilise StarTraq's Dome back-office processing software to upload evidence packages from the DDS, which will be presented for verification on one easy-to-use screen.

Jenoptik will perform a two-stage verification process, and then Dome will facilitate access for police to perform a final approval.

“Using mobile phones or other devices while driving is distracting and greatly increases the chance of being involved in an accident," says Sam Iglewski, MD of Jenoptik Australia.

This particularly dangerous driver behaviour has been an ever-increasing issue during the past years. Our partnership with StarTraq makes a compelling economic and operational business case for the DJCS, and we are looking forward to working with them to deliver the road safety objectives."

Allan Freinkel, chairman of StarTraq, says he is "excited at the global possibilities this strategic contract presents".

For more information on companies in this article

Related Content

  • Harnessing the power of smart technology
    June 28, 2018
    Keeping the public safe in a changing world requires smart thinking and sensible deployment of technology. Peter Jones of Hitachi Europe examines some available options From human threats, such as terrorism, to digital threats like hacking, the growing sophistication of crime is posing serious challenges to public safety. At the same time, mass urbanisation threatens to exacerbate these problems as there are more people to keep safe. According to a new whitepaper from Hitachi and Frost & Sullivan, Public
  • Smarter cameras, better outcomes from Redspeed International
    March 20, 2024
    Redspeed International is pioneering the next generation of advanced road safety solutions through applied AI and advanced camera technology as visitors to its stand will learn firsthand.
  • Jenoptik highlights Vector ANPR cameras
    April 4, 2016
    Jenoptik, the international solution provider for global traffic safety, is highlighting its Vector ANPR cameras which are a vital tool used by police and security forces around the globe. Operated in temporary and long-term installations, Vector is able to rapidly identify and report on vehicles of interest. Working as stand-alone units, or part of a wide ANPR network, Vector provides a 24/7 monitoring capability, with each camera capable of capturing thousands of plate reads every day.
  • Machine vision - cameras for intelligent traffic management
    January 25, 2012
    For some, machine vision is the coming technology. For others, it’s already here. Although it remains a relative newcomer to the ITS sector, its effects look set to be profound and far-reaching. Encapsulating in just a few short words the distinguishing features of complex technologies and their operating concepts can sometimes be difficult. Often, it is the most subtle of nuances which are both the most important and yet also the most easily lost. Happily, in the case of machine vision this isn’t the case: