arxiv_cv 96% Match Research Paper Computer Vision Researchers,Sports Analytics Professionals,AI Engineers,Robotics Engineers (for player tracking) 1 month ago

SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection

computer-vision › object-detection

📄 Abstract

Abstract: In soccer video analysis, player detection is essential for identifying key events and reconstructing tactical positions. The presence of numerous players and frequent occlusions, combined with copyright restrictions, severely restricts the availability of datasets, leaving limited options such as SoccerNet-Tracking and SportsMOT. These datasets suffer from a lack of diversity, which hinders algorithms from adapting effectively to varied soccer video contexts. To address these challenges, we developed SoccerSynth-Detection, the first synthetic dataset designed for the detection of synthetic soccer players. It includes a broad range of random lighting and textures, as well as simulated camera motion blur. We validated its efficacy using the object detection model (Yolov8n) against real-world datasets (SoccerNet-Tracking and SportsMoT). In transfer tests, it matched the performance of real datasets and significantly outperformed them in images with motion blur; in pre-training tests, it demonstrated its efficacy as a pre-training dataset, significantly enhancing the algorithm's overall performance. Our work demonstrates the potential of synthetic datasets to replace real datasets for algorithm training in the field of soccer video analysis.

Key Contributions

Introduces SoccerSynth-Detection, the first synthetic dataset specifically designed for soccer player detection. The dataset features diverse random lighting, textures, and simulated camera motion blur. It demonstrates the efficacy of this synthetic dataset by using Yolov8n, showing it matches performance on real-world datasets and significantly outperforms them in images with motion blur.

Business Value

Enables the development of more robust and versatile soccer player detection systems, crucial for automated sports analytics, coaching tools, and broadcast enhancements. Synthetic data generation can reduce the cost and time associated with collecting and annotating real-world data.

Paper Metadata

Innovation Type

Dataset Creation

Deployment Feasibility

The use of synthetic data generation techniques makes it feasible to create large, diverse datasets for training robust object detection models like Yolov8n, which are themselves deployable.

Limitations Addressed

Scarcity and lack of diversity in existing soccer player detection datasets (e.g., SoccerNet-Tracking, SportsMOT); copyright restrictions limiting real-world data availability; challenges posed by occlusions, varied lighting, and motion blur.

Performance Gains

Synthetic dataset matched performance of real datasets in transfer tests.,Significantly outperformed real datasets in images with motion blur.

Technical Tags

Synthetic DataSoccer Player DetectionObject DetectionYolov8nDataset GenerationComputer VisionData AugmentationTransfer LearningMotion BlurReal-world Datasets

Research Topics

Computer VisionMachine LearningData GenerationSports AnalyticsObject Detection

Methods & Architectures

Synthetic Dataset GenerationObject Detection (Yolov8n)Transfer LearningData Augmentation (lighting, textures, motion blur) Yolov8n

Applications & Tasks

Sports Analytics Video Analysis Computer Vision Data Augmentation Object DetectionDataset ScarcityData Diversity Detecting soccer players in video footageOvercoming limitations of existing soccer datasets (lack of diversity, occlusions)

Datasets & Benchmarks

Datasets

SoccerSynth-Detection, SoccerNet-Tracking, SportsMOT

Related Fields

Sports TechnologySynthetic Data GenerationVideo Object DetectionData Augmentation Techniques

Keywords

synthetic datasoccer detectionplayer detectionobject detectionYolov8ndatasetcomputer visionsports analyticsmotion blurtransfer learningdata augmentationvideo analysis

Academic Context

#Computer Vision#Machine Learning#Data Generation#Sports Analytics#Object Detection

Commercial Potential

Potential Products

Automated soccer performance analysis softwarePlayer tracking systems for broadcastsAI-powered coaching tools

Target Industries

SportsMedia and EntertainmentTechnology

Use Case Examples

Tracking player positions and movements during a soccer matchIdentifying key players or formations automatically

Competitive Edge

Addresses the critical bottleneck of data availability and diversity in soccer player detection by providing a novel synthetic dataset that demonstrates superior performance, especially under challenging conditions like motion blur.

Resource Requirements

Compute Needs

Moderate to High, for training Yolov8n and potentially for generating synthetic data.

Data Requirements

The paper introduces a synthetic dataset; real-world datasets (SoccerNet-Tracking, SportsMOT) are used for comparison.

Deployment Constraints

Real-time processing for live video analysis, robustness to varying camera angles and lighting conditions.

Scalability

Synthetic data generation can be scaled to create arbitrarily large datasets.

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