Mearka - Architecting and evaluation of a Sports Video Tagging Software Toolkit

Abstract

In the past decade, substantial advancements have been achieved in effectively utilizing video surveillance and associated analysis technologies within the realm of sports. This progress has been particularly noteworthy in elite sports, where the exploitation of athletes’ digital footprints for sports analytics has emerged as a catalytic factor, ushering in a paradigm shift in comprehending and formulating strategic approaches to the game.

The architecture of sports video analytics systems can be broadly categorized into (1) tagging and (2) analysis. Tagging involves annotating metadata to specific video sequences and events, and this tagged metadata is subsequently utilized in the causal analysis process.

Multiple enterprise solutions are available today for recording videos, and positions and producing tagged data for the top teams. The issue is that they are often expensive, time-delayed metadata, and the sports organizations do not control where the data is stored or how the analytics company uses it. The alternative to enterprise solutions is manually generating the soccer metadata, which is time-consuming and possibly impossible if, for example, one wants to tag every player’s position throughout a game.

This thesis presents Mearka, a distributed soccer tagging system based on cheap common-off-the-shelf components. It allows for tagging events LIVE during a soccer game through the Mearka-app, as well as generating player position metadata with time offsets into a user-uploaded video through the Mearka web-interface, automatically detected using machine learning. After detection, it is possible to download the soccer metadata as a JSON file through the web-interface.

The experiment results demonstrate that Mearka can complete the detection of players’ positions from a 90 minutes soccer game within 12 hours after detection is started, with a video resolution of 1920x1080 at 25FPS. Expanding Mearka to only detect on every 10th frame could potentially make Mearka a viable real-time tagging option, as it is able to detect on ≈3 frames per second, and a turnaround of 12 hours detects on every single video frame.