AWS Media and Entertainment global

TRACAB

Real-time Analytics

TRACAB needed more scalable compute and lower-latency data delivery for computer-vision sports tracking as its features became more complex and its on-premises arena-based setup limited deployment speed and accuracy.

average latency of 0.85 seconds
more than 2,000 matches per year
nearly 200 stadiums
over 255 million data points per match

Reported impact

Business outcome

TRACAB increased collected data to more than 255 million data points per match, achieved average latency of 0.85 seconds, reduced manual work by shifting from two onsite operators to one remote operator, and expanded to nearly 200 stadiums running more than 2,000 matches per year.

  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

TRACAB migrated its sports-tracking platform from on-premises arena servers to AWS so it could scale computer-vision processing and deliver richer match insights in near real time. Video and tracking data from multiple cameras is sent to AWS, where deep-learning models run on Amazon EC2 G4, G5, and G6 GPU instances to identify players, the ball, and new skeleton-tracking signals. Low-latency state and event access is supported by Amazon ElastiCache for Redis and Amazon DynamoDB, letting downstream consumers such as broadcast teams, referees, coaches, and fans use the data quickly. The cloud architecture also let TRACAB automate work that once required two onsite operators, support a remote operator model, and continuously deploy new features such as semi-automated offside technology and virtual cameras across nearly 200 stadiums.

Data flow

Multi-camera match video and tracking data is streamed to AWS, processed by deep-learning models on GPU-backed Amazon EC2 instances, stored for low-latency access in Amazon DynamoDB and Amazon ElastiCache for Redis, and then delivered as near-real-time insights to broadcasters, officials, coaches, players, and fans.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon EC2 G4 Instances Provide GPU-backed compute for TRACAB's computer-vision workloads.
    • Amazon EC2 G5 Instances Extend high-performance GPU capacity for more advanced tracking models.
    • Amazon EC2 G6 Instances Support newer GPU-intensive processing for enhanced sports tracking features.
  2. Storage
    • Amazon DynamoDB Stores tracking data in a serverless database for low-latency access.
  3. Serving
    • Amazon ElastiCache for Redis Provides low-latency access to tracking and event data.

Architecture clues

  • Added near real-time skeleton tracking in 2023.
  • New use cases include semi-automated offside technology and virtual cameras.
  • Started streaming video directly to AWS in 2022.

Evidence from the source

By sending tracking data to the cloud, TRACAB could use AI powered by advanced deep learning models on AWS to perform the task with a single remote operator.
TRACAB can collect more than 255 million data points per match while achieving an average latency of 0.85 seconds.
TRACAB has rolled out its solution to nearly 200 stadiums, which collectively run more than 2,000 matches per year.
TRACAB next enhanced its solution in 2023 with near real-time skeleton tracking.
Transparency Evidence & methodology 100/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    TRACAB customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/tracab
    Source type
    Vendor-published customer story
    Published
    Sep 20, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Highperformr

    Where is Tracab Located? HQ, Global Offices & Company Insights

    Independent secondary
    https://www.highperformr.ai/company/tracab

    The public company page lists 'Total employees' as '75' and 'Country' as 'Sweden'.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.