Case Study Machineroad · IT Consulting & Development | Custom Software, SAP, Microsoft

Case Study Machineroad

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Machineroad Case Study

In this case study, we helped machineroad by developing a bespoke mobile-based application to help improve the Cricket bowling skills for their user base. The app helps to measure the ball speed it allows user to do a video recording and creates an image snippet for the end user showing trajectory.  We also added detailed level analytics to help them see their progress week on week and month on month and some salient feature of the app includes Gamification and Leaderboard. 

In this white paper, we explain the journey which was taken. How we decided on the resources and logic and to ensure costs are optimized on the cloud as they scale up number of users and consume more cloud resources. Attached PDF has more details

  1. Scope and Objective
  2. Requirement Gathering 
  3. Approach
    1. POCs
    2. UI/UX
  4. The Journey
  5. Technology we used

Solution

  • Mobile app for Android and iOS platforms which can allow users to record Cricket bowling video to get bowling speed, line/length & trajectory
  • A monthly subscription service to get more detailed insights of the training sessions
  • AI & ML based video processing to analyse the recorded videos and give consistent and accurate results
  • Serverless cloud platform which can scale to process hundreds of videos in parallel

Challenges

We encountered some issues as below: 

  • Achieve the similar results from 1 camera, which in actual games are achieved using hawk-eye technique that involves 6
  • Ability to process videos recorded in different environments, under different lighting conditions, different pitch.
  • Provide a way to user to help them setting the phone correctly to minimize user error
  • Device Manufacturer based implementation to use native device features to record slow-mo videos and make it work on different android/ iOS devices
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Results

Accomplishments

 Deliverable:

  • Publishing the app on Play store and App Store initially for the beta users and then open for all user
  • Deployment using AWS architecture on the cloud to setup a scalable and optimized backend system
  • Integration with Native camera capabilities with advanced machine learning algorithm incorporated to get accurate speed and trajectories.

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