Skip to content
Blueprint Technologies - Data information specialists
  • What we do

      Technology Solutions

      Application development
      Cloud and infrastructure
      Data governance
      Data migration
      Data science and analytics
      Ethos privacy platform
      IoT enablement
      Modern data estate
      Video analytics

      Solution Accelerators

      Data Catalog
      Data Loader
      Data Sharing Portal
      Datalake Query Editor
      Ethos Privacy Program
      Lakehouse Monitor

      Supportive Services

      Privacy consulting services
      Support engineering
      Localization

      Partnerships

      Databricks Partnership

      We specialize in using the power of the Databricks Lakehouse to help our clients solve real-world business problems

      Learn more
  • Our approach
  • Our work
  • Insights
  • Careers
Connect
Blueprint Technologies - Data information specialists
Back to insights

I feel the need. The need for speed…dee data

By Gary Nakanelua

As managing director of Innovation at Blueprint Technologies, I have the pleasure of working directly with some of the most talented data scientists in the world, both within our company and through our various partners. A common theme I have found in projects involving data science is the need for significant amounts of data.

Recently, we worked with our largest partner, Microsoft, on a video analytics project. It was an incredible opportunity to experiment with Azure for video processing and analysis. The case study for this project will be published soon, so rather than detail out the solution, I’ll cover a problem we had to overcome early in the project: availability of relevant video data.

We had 60 days to go from whiteboard to market with a video analytics solution that solved for a specific use case within a specific industry. We needed overhead video footage of people and vehicles within a city environment. After a bit of Google-Fu, we found quite a few overhead static imagery datasets but we needed video. The few video datasets we did find lacked the desired consistency. We had to figure out something different. Quickly.

We experimented with generating the video we needed using drones. The approach lacked the traffic density we needed.

Attempts to capture footage of live traffic resulted in warnings by local law enforcement on the use of civilian drones in high traffic areas. It was time to try something different. Or get arrested.

Previously, we had success generating training data for machine learning models using video games. In fact, at the Apache Spark + AI Summit a few years ago, we presented our research in training collision detection for an autonomous drone experiment using Doom.

Due to the ability to build a world to fit our needs, we originally intended to use Minecraft. In 2014, Microsoft acquired Mojang, the game studio that created Minecraft. Two years later, Microsoft publicly unveiled Project Malmo, “a sophisticated AI experimentation platform built on top of Minecraft, and designed to support fundamental research in artificial intelligence.” You can check out Project Malmo here. However, Minecraft lacked the vehicles and associated driving behaviors we needed for the project.

We were introduced to AirSim, an open source simulator for autonomous vehicles built on the Unreal Engine from Microsoft AI & Research. Based upon the demos, it appeared to have everything we needed to generate our video data. You can check out AirSim here. However, building AirSim on a MacBook was proving to take more time than anticipated. The documentation did note that “It should be possible to build AirSim on OSX as well, but it isn’t actively tested.” Yet again, we had to find a different way.

The solution to our problem turned out to be one of the most ambitious simulations of a city available: Grand Theft Auto V. It was created by Rockstar North and the studio took great care in attempting to recreate Los Angeles (Los Santos as it is referred to in the game). The studio sent out multiple research teams throughout Los Angeles and shot over 250,000 images and hours of video. From the Los Angeles International Airport and Beverly Hills to landmarks such as the Hollywood sign and the Griffith Observatory, Grand Theft Auto V had all the elements we needed to generate our video data.

The game includes a director mode, which allowed us to control traffic density, pedestrian population, time of day, weather, and camera angle. Camera control would prove to be the most beneficial as our early attempts in the game started with hovering over a particular area of the city in a helicopter.

This approach saved weeks of time. We avoided having to program traffic simulations and randomization patterns. It provided high enough fidelity that we avoided having to travel to physical locations to film the video footage we need (and avoid getting arrested). It provided the flexibility necessary to generate hours worth of relevant training and testing data. Using this data, we were able to train various algorithms for object identification and tracking. In addition, the video data is used to train activity similarity models and improve overall accuracy of the models.

Although using the game to train machine learning models may not be what the designers had in mind, the approach proved to be a quick and efficient way of generating pedestrian and vehicles-in-motion activity. Unfortunately, we didn’t have programmatic access to the game (we used a Xbox) so we had to actually play the game to get the locations and activity we wanted. The sacrifices we make in the name of data science…

Let's build your future.

Share with your network

You may also enjoy

Article

Future-Proofing Your Business: Data acquisition best practices

Organizations looking to future-proof their business will find success or failure determined by a few critical elements within their strategy—the first being the development of a robust data estate. Prioritizing this has a significant impact on business, specifically through cost savings and improved scalability.

Article

Building Ethical and Transparent Global AI Standards

The AI landscape is hitting new levels of growth, and legislators are taking notice. AI legislation may be in its early stages, but proposed regulations offer insights into the future of AI.
Blueprint Technologies - Data information specialists

What we do

  • Application development
  • Cloud and infrastructure
  • Data governance
  • Data migration
  • Data science and analytics
  • IoT enablement
  • Localization
  • Modern data estate
  • Privacy consulting services
  • Support engineering
  • Video analytics
Menu
  • Application development
  • Cloud and infrastructure
  • Data governance
  • Data migration
  • Data science and analytics
  • IoT enablement
  • Localization
  • Modern data estate
  • Privacy consulting services
  • Support engineering
  • Video analytics

Our approach

  • Business strategy
  • Course of Action Assessment
  • Facilitated innovation
  • Managed services
  • Product development
  • Project Definition Workshop
  • Proof of Concept
  • Solution development
Menu
  • Business strategy
  • Course of Action Assessment
  • Facilitated innovation
  • Managed services
  • Product development
  • Project Definition Workshop
  • Proof of Concept
  • Solution development

Our work

Insights

Careers

Accelerator Support

Contact us

Linkedin Youtube Twitter Facebook Instagram
© 2022 Blueprint Technologies, LLC. 2600 116th Avenue Northeast, First Floor
Bellevue, WA 98004

All rights reserved.
Media Kit

Employer Health Plan

Privacy Notice
  • What we do
  • Our approach
  • Our work
  • Insights
  • Careers
  • Connect
Menu
  • What we do
  • Our approach
  • Our work
  • Insights
  • Careers
  • Connect
Follow
  • LinkedIn
  • Youtube
  • Twitter
  • Facebook
  • Instagram
Menu
  • LinkedIn
  • Youtube
  • Twitter
  • Facebook
  • Instagram