Σεμινάριο: "Statistical Knowledge Distillation for Tiny Object Detection in Tennis Videos Analysis"

ΚΥΚΛΟΣ ΣΕΜΙΝΑΡΙΩΝ ΣΤΑΤΙΣΤΙΚΗΣ 2026-2027

Spekaer: Andrea Mecca, PhD Student, University of Florence, Florence, Italy, and Research Grant Holder, University of Siena, Siena, Italy

Statistical Knowledge Distillation for Tiny Object Detection in Tennis Videos Analysis

ΑΙΘΟΥΣΑ: 401, 4^ος Όροφος Κτίριο Ευελπίδων

ΠΕΡΙΛΗΨΗ

In tennis video analysis, accurate ball detection is essential for match evaluation and performance analysis. Professional contexts often rely on costly high-resolution, high-frame-rate camera infrastructures, which are not available to amateurs. Deep-learning models such as TrackNet perform well in detecting small and fast-moving objects in sports such as tennis and badminton. However, their computational cost can hinder deployment in resource-constrained and real-time applications. We formulate knowledge distillation from a statistical perspective, treating pixel-level intermediate outputs from a TrackNet v1 teacher neural network as responses in a simpler and more transparent statistical student model. The resulting response variable is a semicontinuous random variable with a large clump at zero and continuous values in the open interval (0,1). Therefore, we adopt a hurdle-model framework, using logistic regression to model the probability of a positive response and beta regression for the conditional distribution of the response given that it is positive. For the logistic regression component, we propose a novel extension of a backfitting-type algorithm that jointly estimates the regression parameters and selects a specific covariate threshold in a data-driven manner using decision stumps. Since the joint analysis of multiple frames yields billions of pixel-level observations, full-data estimation is computationally prohibitive. Consequently, we also investigate informative subsampling techniques for logistic regression, with particular focus on adaptations of the local case-control subsampling method.