Michigan, USLocation
3-7Team Size
Sports AnalyticsIndustry
The focus of our work was to understand each sport that our customer analyzed, memorize team compositions, and tag/label participating athletes seen from the game photographs to their correct names using bounding boxes and image segmentation techniques.
About The Company
Our client is an award-winning sports analytics AI startup that has analyzed 59 Million+ athletes’ bodies and faces across team sports and race events across the globe.
They use a combination of deep learning, scalable cloud infrastructure, and elastic search. Within seconds their AI identifies all legible jersey/bib numbers, player faces, and branded gear information from photos/video captured by photographers in real time.
Sporting events include marathons, and team sports like football, soccer, ice hockey, baseball, lacrosse, and basketball.
The Tech Stack
We worked on the client’s in-house customized data labeling tool to annotate the images. The annotation techniques used were bounding boxes and segmentation. The real-time communication channel while the game was ongoing, was our Slack channel with the customer where we received links to the imagery and business rules for the time-sensitive task at hand.
Team Composition
Helped by 1 Senior Project Manager, the team comprised 2 Data Annotators in the long term and 3 data annotators for occasional gaming season spikes.
The Problem Statement
The customer’s training data pipeline compiles up to 30,000 images per event from solo & team sports.
For our customer to have a high-confidence model, data is required to be tagged on the go in a global time zone. Tagging consists of athlete faces, jersey numbers, and other gear items that need to be annotated on the fly at times to optimize Algorithm results.
This required our customer to have a team that understands the game in question, teams, a grasp of players participating, and fast-paced real-time communication around the clock.
Also required pixel-perfect annotation of 100% area in every image including the tiniest of details via segmentation annotation.
The Challenge
Netsmartz’s Data annotators had to go through 4000+ images in bibs cleaning, 1000+ images in bounding boxes, and numerous images for segmentation daily. The challenge was not only to have a near 100% accuracy but also to give them domain knowledge of every sport.
The Solution
The Team was set up with a fast-paced learning curve for business rules and player identification with a careful shortlisting of the team for the task. Data Annotators were trained by educating them with the respective game videos, player roasters, and memory tests.
The shifts and holiday roaster of the data annotators was planned in a way that it became into data annotators passing the baton to each other wherein there was someone or the other behind the desk 24×7 365 days.
The data annotators today identify players and match them to their names and clubs/teams as a fan would do in real time. Annotators even update the customer’s database in case of player transfers and retirement every week by looking up Sporting news.
The Segmentation annotation has been taken over by our skilled annotators who have worked on numerous commercial and open-source data annotation tools and have tagged millions of images/videos across use cases to provide a near 100% accuracy.
Our Process
Netsmartz followed the following process to fulfill the requirements of the client:
Mapping & Identifying Appropriate Resources
We took interviews with approx 10 in-house candidates and selected the most appropriate ones who had a natural interest in Sports. They were additionally interviewed on the skills of the English language and previous fast-paced annotation skills. Thereon, Netsmartz was first put to test with other workforce partners by the customer in a two-week assignment. Netsmartz came across as the No. 1 choice for our customer.
Team Engagement & Continuous Training
The team was provided training for a week before they went into the production environment. The learning curve enabled them to generate 95% accuracy over the first two weeks.
Consistent Client Feedback & Action
Initially the Project Manager met the client daily to gather feedback, and thereon weekly. Due to a time zone difference and alignment of shifts, we were able to garner daily client feedback for improvement and implement it in a way that progress was seen in every day of operations.
The Result
The primary achievements of this project were:
- We eased resource budget and operational hassle for our customer’s core team by tagging up to 800 images per shift along with bibs cleaning of 2,000-3,000 images per day. This speed was achieved in the initial 3 weeks.
- In addition to their bread & butter market of marathons, our client moves confidently relying on Netsmartz as they take on team sports such as NBA, Major League Soccer, NFL, Indian Premier League Cricket, and Ice Hockey League. All with speed, accuracy, time zone, and cost efficiency, without fretting about having an expensive team of sports enthusiasts who could handle their data annotation.
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