Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos

• URL: http://arxiv.org/abs/2505.09455v1
• Date: Wed, 14 May 2025 15:05:36 GMT
• Title: Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos
• Authors: Jeremie Ochin, Raphael Chekroun, Bogdan Stanciulescu, Sotiris Manitsaris,
• Abstract summary: State-of-the-art,temporal action detection methods show promising results for extracting events from broadcast videos.<n>Many false positives could be resolved by considering a broader sequence of actions and game-state information.<n>We address this by reasoning at the game level and improving STAD through the addition of a denoising sequence task.
• Score: 1.4249472316161877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art spatio-temporal action detection (STAD) methods show promising results for extracting soccer events from broadcast videos. However, when operated in the high-recall, low-precision regime required for exhaustive event coverage in soccer analytics, their lack of contextual understanding becomes apparent: many false positives could be resolved by considering a broader sequence of actions and game-state information. In this work, we address this limitation by reasoning at the game level and improving STAD through the addition of a denoising sequence transduction task. Sequences of noisy, context-free player-centric predictions are processed alongside clean game state information using a Transformer-based encoder-decoder model. By modeling extended temporal context and reasoning jointly over team-level dynamics, our method leverages the "language of soccer" - its tactical regularities and inter-player dependencies - to generate "denoised" sequences of actions. This approach improves both precision and recall in low-confidence regimes, enabling more reliable event extraction from broadcast video and complementing existing pixel-based methods.

Related papers

• FDDet: Frequency-Decoupling for Boundary Refinement in Temporal Action Detection [4.015022008487465]
  Large-scale pre-trained video encoders tend to introduce background clutter and irrelevant semantics, leading to context confusion and boundaries.<n>We propose a frequency-aware decoupling network that improves action discriminability by filtering out noisy semantics captured by pre-trained models.<n>Our method achieves state-of-the-art performance on temporal action detection benchmarks.
  arXiv  Detail & Related papers  (2025-04-01T10:57:37Z)
• Game State and Spatio-temporal Action Detection in Soccer using Graph Neural Networks and 3D Convolutional Networks [1.4249472316161877]
  Soccer rely on two data sources: the player positions on the pitch and the sequences of events they perform.<n>We propose atemporal action detection approach that combines visual and game state analytics via Graph Neural Networks trained end-to-end with state-of-the-art 3D CNNs.
  arXiv  Detail & Related papers  (2025-02-21T13:41:38Z)
• A Simple and Effective Temporal Grounding Pipeline for Basketball Broadcast Footage [0.0]
  We present a reliable temporal grounding pipeline for video-to-analytic alignment of basketball broadcast footage. Our method aligns a pre-labeled corpus of play-by-play annotations containing dense event annotations to video frames, enabling quick retrieval of labeled video segments.
  arXiv  Detail & Related papers  (2024-10-30T17:27:44Z)
• Towards Active Learning for Action Spotting in Association Football Videos [59.84375958757395]
  Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns. Current algorithms face significant challenges when learning from limited annotated data. We propose an active learning framework that selects the most informative video samples to be annotated next.
  arXiv  Detail & Related papers  (2023-04-09T11:50:41Z)
• Boundary-Denoising for Video Activity Localization [57.9973253014712]
  We study the video activity localization problem from a denoising perspective. Specifically, we propose an encoder-decoder model named DenoiseLoc. Experiments show that DenoiseLoc advances %in several video activity understanding tasks.
  arXiv  Detail & Related papers  (2023-04-06T08:48:01Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection [3.7709686875144337]
  We present a two-stage paradigm to detect what and when events happen in soccer broadcast videos. Specifically, we fine-tune multiple action recognition models on soccer data to extract high-level semantic features. This approach achieved the state-of-the-art performance in both two tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2 Challenge.
  arXiv  Detail & Related papers  (2021-06-28T08:00:21Z)
• Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts [86.56462654572813]
  We focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game. We propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge. We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action spotting.
  arXiv  Detail & Related papers  (2021-04-14T11:09:03Z)
• Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation [79.98992138865042]
  We present an augmented transformer with adaptive graph network (ATAG) to exploit both long-range and local temporal contexts for TAPG. Specifically, we enhance the vanilla transformer by equipping a snippet actionness loss and a front block, dubbed augmented transformer. An adaptive graph convolutional network (GCN) is proposed to build local temporal context by mining the position information and difference between adjacent features.
  arXiv  Detail & Related papers  (2021-03-30T02:01:03Z)
• SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos [71.72665910128975]
  SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset. We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
  arXiv  Detail & Related papers  (2020-11-26T16:10:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.