Deep learning for action spotting in association football videos (2410.01304v1)

Abstract: The task of action spotting consists in both identifying actions and precisely localizing them in time with a single timestamp in long, untrimmed video streams. Automatically extracting those actions is crucial for many sports applications, including sports analytics to produce extended statistics on game actions, coaching to provide support to video analysts, or fan engagement to automatically overlay content in the broadcast when specific actions occur. However, before 2018, no large-scale datasets for action spotting in sports were publicly available, which impeded benchmarking action spotting methods. In response, our team built the largest dataset and the most comprehensive benchmarks for sports video understanding, under the umbrella of SoccerNet. Particularly, our dataset contains a subset specifically dedicated to action spotting, called SoccerNet Action Spotting, containing more than 550 complete broadcast games annotated with almost all types of actions that can occur in a football game. This dataset is tailored to develop methods for automatic spotting of actions of interest, including deep learning approaches, by providing a large amount of manually annotated actions. To engage with the scientific community, the SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. Thanks to our dataset and challenges, more than 60 methods were developed or published over the past five years, improving on the first baselines and making action spotting a viable option for the sports industry. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry.

• The paper introduces a deep learning framework that precisely spots actions in lengthy football videos by leveraging the comprehensive SoccerNet dataset.
• It compares feature-based methods using pre-trained models with end-to-end approaches incorporating transformers for improved temporal dynamics.
• The study uses tailored metrics like a-mAP to evaluate temporal localization precision, providing a new benchmark for sports analytics.

Deep Learning for Action Spotting in Association Football Videos

The paper provides a comprehensive exploration into using deep learning for action spotting in association football videos, emphasizing the need for accurately identifying and localizing actions within long, untrimmed video streams. This task is instrumental for sports analytics, coaching, and enhancing fan engagement. Prior to 2018, the dearth of large-scale datasets hindered benchmarking in this domain, but this issue was addressed through the introduction of the SoccerNet dataset.

SoccerNet Dataset and Its Evolution

SoccerNet was introduced as the largest dataset tailored for action spotting in football, comprising over 550 fully annotated broadcast games. This dataset forms the backbone for various challenges and competitions that motivate the development of state-of-the-art methods. The dataset's architecture has undergone significant expansions to accommodate the sophistication of video understanding models. Initially focused on three action classes — goals, cards, and substitutions — it evolved to include more complex annotations in SoccerNet-v2, and more recently, specific tasks like Ball Action Spotting targeting ball interactions were introduced.

Methodological Framework

The methodological framework for action spotting comprises three key stages: the backbone for feature extraction, the neck for feature refinement, and the head for temporal localization and classification. This paper examines both feature-based methods and end-to-end approaches, demonstrating a shift in the use of pre-trained models like ResNet for feature extraction to end-to-end models fine-tuned for specific tasks.

Feature-Based Methods

Feature-based methods leverage pre-extracted features from models trained on extensive datasets, utilizing pooling strategies and context-aware losses to improve temporal localization accuracy. Notable methods include the NetVLAD and its advanced version, NetVLAD++, which adapts context-awareness for enhanced action spotting.

End-to-End Methods

End-to-end models, such as E2E-Spot, are trained holistically, optimizing the entire architecture directly for the action spotting task. These models offer improved performance by integrating temporal dynamics into the backbone and enhancing long-term temporal reasoning through sophisticated architectures like transformers.

Evaluation and Metrics

Evaluation hinges on metrics specifically tailored for the temporal accuracy of action spotting. The average-mean Average Precision (a-mAP) and its variants, including mAP@t, provide crucial performance indicators across various action classes and offer insights into model efficacy in both loose and tight temporal constraints.

Implications and Future Directions

The advancements in action spotting have broad applications beyond football, offering insights into automated decision-making in sports and potential crossover into general video understanding. The iterative progression of the challenges instigated by the SoccerNet initiative fosters an environment of continuous improvement, pushing the boundaries of AI capabilities in sports analysis. Future developments may explore deeper integration of multi-modal data and self-supervised learning to manage the constraints of annotation scarcity.

In conclusion, the paper delineates critical progressions in deep learning applications for soccer video analysis, underscoring the pivotal role of datasets and open challenges in propelling research and practical applications in this dynamic sector.