Towards Automated Key-Point Detection in Images with Partial Pool View

TL;DR

This paper introduces a pool model with invariant key-points and studies their detectability in images with partial pool views to improve swimmer tracking in sports analytics.

Contribution

It proposes a novel pool model with invariant key-points and analyzes their detectability in partial view images, addressing challenges in swimming data collection.

Findings

Pool model with invariant key-points for swimming analytics

Detectability analysis of key-points in partial pool views

Addresses challenges in swimmer tracking with partial views

Abstract

Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable manner. This work is aimed at alleviating some of the challenges encountered in the collection of adequate swimming data. Past works on this subject have shown that the detection and tracking of swimmers is feasible, but not without challenges. Among these challenges are pool localization and determining the relative positions of the swimmers relative to the pool. This work presents two contributions towards solving these challenges. First, we present a pool model with invariant key-points relevant for swimming analytics. Second, we study the detectability of such key-points in images with partial pool view, which are challenging but also quite common in swimming race videos.