SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos

TL;DR

This paper introduces SoccerKDNet, a knowledge distillation framework for soccer action recognition that achieves high accuracy on a new dataset, enabling efficient deployment in resource-limited scenarios.

Contribution

The paper presents a novel end-to-end knowledge distillation approach with a unique loss parameterization and introduces the SoccerDB1 dataset for soccer action classification.

Findings

Achieved 67.20% validation accuracy on SoccerDB1

Outperformed prior methods significantly

Model generalizes well to new datasets

Abstract

Classifying player actions from soccer videos is a challenging problem, which has become increasingly important in sports analytics over the years. Most state-of-the-art methods employ highly complex offline networks, which makes it difficult to deploy such models in resource constrained scenarios. Here, in this paper we propose a novel end-to-end knowledge distillation based transfer learning network pre-trained on the Kinetics400 dataset and then perform extensive analysis on the learned framework by introducing a unique loss parameterization. We also introduce a new dataset named SoccerDB1 containing 448 videos and consisting of 4 diverse classes each of players playing soccer. Furthermore, we introduce an unique loss parameter that help us linearly weigh the extent to which the predictions of each network are utilized. Finally, we also perform a thorough performance study using various changed hyperparameters. We also benchmark the first classification results on the new SoccerDB1 dataset obtaining 67.20% validation accuracy. Apart from outperforming prior arts significantly, our model also generalizes to new datasets easily. The dataset has been made publicly available at: https://bit.ly/soccerdb1