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Sports analytics has been an up-and-coming field of research amongprofessional sporting organizations and academic institutions alike. With theinsurgence and collection of athlete data, the primary goal of such analysis isto improve athletes' performance in a measurable and quantifiable manner. Thiswork is aimed at alleviating some of the challenges encountered in thecollection of adequate swimming data. Past works on this subject have shownthat the detection and tracking of swimmers is feasible, but not withoutchallenges. Among these challenges are pool localization and determining therelative positions of the swimmers relative to the pool. This work presents twocontributions towards solving these challenges. First, we present a pool modelwith invariant key-points relevant for swimming analytics. Second, we study thedetectability of such key-points in images with partial pool view, which arechallenging but also quite common in swimming race videos.

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