A Simple Temporal Grounding Pipeline for Aligning Basketball Broadcast Footage with Play-by-Play Annotations

Core Concepts

This paper introduces a simple yet effective pipeline for aligning basketball broadcast footage with play-by-play annotations by extracting time-remaining and quarter values from video frames using object detection and OCR, aiming to facilitate the development of large, multi-modal datasets for sports action recognition.

Abstract

Bibliographic Information:

Harris, L. (2024). A Simple and Effective Temporal Grounding Pipeline for Basketball Broadcast Footage. arXiv preprint arXiv:2411.00862v1.

Research Objective:

This paper aims to develop a reliable and efficient pipeline for temporally aligning basketball broadcast footage with corresponding play-by-play annotations. This alignment is crucial for creating large, multi-modal datasets that can be used to train and evaluate video models for sports action recognition.

Methodology:

The proposed pipeline employs a two-staged approach: text detection and text recognition. First, a fine-tuned YOLOv8l object detection model, trained on a custom dataset of basketball broadcast frames, identifies semantic text regions (quarter and time remaining) within each frame. Subsequently, the detected regions are cropped and processed by the PaddleOCR library for text recognition. To ensure temporal consistency and handle potential occlusions, a denoising algorithm and interpolation techniques are applied to the extracted timestamps. The pipeline is further optimized for large-scale deployment through parallelization across multiple CPU threads.

Key Findings:

The paper demonstrates the effectiveness of the proposed pipeline in accurately aligning basketball broadcast footage with play-by-play annotations. The use of end-to-end text localization and readily available OCR tools simplifies the process and ensures reproducibility. The authors highlight the pipeline's potential to expedite the development of large, multi-modal datasets for action recognition models.

Main Conclusions:

The paper concludes that the proposed temporal grounding pipeline offers a practical and efficient solution for aligning basketball broadcast footage with play-by-play annotations. The simplicity of the approach, combined with the use of open-source libraries and deep learning methods, makes it easily adaptable for various use cases and sub-domains within sports analytics.

Significance:

This research contributes to the field of sports video analysis by providing a streamlined method for temporal grounding, a crucial step in creating annotated datasets for training and evaluating video understanding models. The proposed pipeline has the potential to accelerate research in sports action recognition and facilitate the development of automated sports analysis tools.

Limitations and Future Research:

The authors acknowledge the need for further testing and quantitative benchmarks to evaluate the robustness of the pipeline across diverse broadcast conditions. Future work could explore the use of custom, in-domain trained text recognition models to enhance accuracy and investigate methods for handling cases where on-screen graphical indicators are absent.

Stats

The pipeline achieves a text recognition accuracy of 93.81% on a random sample of frames before post-processing.
The video corpus used for testing includes footage from various basketball leagues, including the NBA, WNBA, Euroleague, NCAA, WNCAA, and American high school basketball leagues.
The authors trained their custom object detection model for 130 epochs.
The video frames are processed at a consistent frame rate of 30 frames per second (fps).

Quotes

"After reviewing the relevant literature, we conclude our text-extraction method for basketball broadcast scenes is superior to existing methods in simplicity of design and ease of implementation."
"We believe our pipeline is adaptable in many use cases and sub-domains. Specifically, we foresee that our work will allow other researchers to quickly and easily develop large, multi-modal datasets for action recognition models."

Key Insights Distilled From

A Simple and Effective Temporal Grounding Pipeline for Basketball Broadcast Footage

by Levi Harris at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.00862.pdf