Automatic Pass Annotation from Soccer VideoStreams Based on Object Detection and LSTM

TL;DR

This paper introduces PassNet, an AI-based system that automatically detects soccer passes from video streams, reducing manual annotation costs and improving accuracy across varied match conditions.

Contribution

The paper presents PassNet, a novel neural network-based method combining object detection and sequence classification to recognize soccer passes automatically.

Findings

PassNet achieves high classification accuracy in diverse scenarios.

Significant improvement over baseline classifiers in pass detection.

Effective in different match conditions, including unseen environments.

Abstract

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each match. These events (e.g., passes, shots, fouls) are collected by human operators manually, constituting a considerable cost for data providers in terms of time and economic resources. In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i.e., passes, from video streams. Our model combines a set of artificial neural networks that perform feature extraction from video streams, object detection to identify the positions of the ball and the players, and classification of frame sequences as passes or not passes. We test PassNet on different scenarios, depending on the similarity of conditions to the match used for training. Our results show good classification results and significant improvement in the accuracy of pass detection with respect to baseline classifiers, even when the match's video conditions of the test and training sets are considerably different. PassNet is the first step towards an automated event annotation system that may break the time and the costs for event annotation, enabling data collections for minor and non-professional divisions, youth leagues and, in general, competitions whose matches are not currently annotated by data providers.