1. / Automotive OEM Gets Accurate Video Labelling for Autonomous Vehicles
Overview

With autonomous car technology cresting the horizon, automakers are working hand-in-hand with IT partners to drive greater innovation and safety margins within every self-driving vehicle. 

One of Germany’s most well-known OEM’s launched a new technology division aimed at perfecting autonomous driving technology. The firm looked to Apexon to help them with a video labelling solution that would tag various objects in and around a car, enabling faster mobility application development.

Problem

While video labelling is not an entirely new technology, this particular project posed certain significant challenges.

  • Handling new work types with relatively unknown business rules and volume flow
  • Working with poor quality video files
  • Delivering a quality solution with a very low scope for error and zero timeline to rectify errors
  • Low resource availability, high attrition rate and short annual hiring timelines
  • Building an ODC setup to work with patented technologies and solutions
Solution

In building and deploying the solution, Apexon engineers used a number of tools to both annotate the video labels and to streamline the hiring process. Solution details include:

  • Tailoring new hiring processes to meet the requirements specific to the received work input format
  • Deploying outcome-based incentive models and cross training as a career progression indicator
  • Using a blend of proprietary toolsets and Philosys Label Editor to fully annotate objects using textural and geometric markers and classes, while delivering them in XML data format – to be used in algorithmic validation and machine learning applications
  • Leveraging the Cvedia platform to enable deep learning vehicle applications via live streaming video footage, and Vatic annotation tool to quickly build massive video sets that are deployable to Amazon cloud
Impact

Apexon successfully hit every milestone and reached all mandated targets over the course of two years, processing over 25 million labels in that time frame. Additionally, we were able to:

  • Establish stringent resource management protocols, including a 10% resource buffer
  • Drastically reduce project hiring times and infrastructure set up costs
  • Deploy a reliable data security framework with scalable options for future business growth
  • Consistently deliver an SLA target of 99.5%, with under 5 defects per 1000 labels

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