Multimodal and Multiview Sensor-based Sports Analysis and Virtual Reality Viewing System for Enhanced Player Tracking and Pose Estimation

Concetti Chiave

A comprehensive system for sports competition analysis and real-time visualization on VR/AR platforms, utilizing multimodal and multiview sensors for precise player tracking and pose estimation.

Sintesi

The proposed system consists of the following key components:

Data Acquisition: Multimodal and multiview sensors, including LiDAR and cameras, are used to collect game data.

Multi-player Tracking: A multimodal detection method is employed to extract 3D bounding boxes of players, fusing features from both point clouds and images. A multimodal data association algorithm is then used to link the detections to previous trajectories, leveraging both geometry and appearance information. A trajectory regain module is also introduced to recover broken trajectories.

Pose Estimation: A voxel-based 3D human pose estimation pipeline, named PointVoxel, is proposed. It fuses point cloud and 2D image features in a unified 3D volumetric representation. An unsupervised domain adaptation training strategy is developed to address the lack of annotated 3D pose data.

Avatar Modeling and VR/AR Visualization: The tracked player positions and poses are used to drive 3D avatar models of the players, which are then visualized in a virtual sports venue. Audiences can watch the game from various perspectives, including following specific players, on VR/AR devices.

Extensive experiments on the newly introduced BasketBall dataset demonstrate the accuracy and robustness of the multi-player tracking and pose estimation components. The visualization results showcase the potential of the proposed system for immersive sports viewing on VR/AR platforms.

Statistiche

The proposed system can achieve 96.16% MOTA and 55.48% HOTA on the BasketBall dataset for multi-player tracking.
On the CMU Panoptic Studio dataset, the PointVoxel pose estimation method achieves 14.44 mm MPJPE and 11.61 mm PA-MPJPE in the supervised setting, outperforming previous state-of-the-art methods.
On the synthetic Player-Sync dataset, the unsupervised PointVoxel achieves 72.92 mm MPJPE and 62.72 mm PA-MPJPE, demonstrating the effectiveness of the proposed unsupervised training strategy.

Citazioni

"Visualization of sports competitions in VR/AR represents a revolutionary technology, providing audiences with a novel immersive viewing experience."
"Combining different sensors such as LiDAR and RGB camera has the potential of boosting the accuracy of 3D human pose estimation performance in multiple situations."
"Manually annotating or capturing 3D poses for multiple individuals in large scenes is challenging, expensive, and hard to ensure generalizability to new scenarios."

Approfondimenti chiave tratti da

Sports Analysis and VR Viewing System Based on Player Tracking and Pose Estimation with Multimodal and Multiview Sensors

by Wenxuan Guo,... alle arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01112.pdf

Domande più approfondite

How can the proposed system be extended to support real-time broadcasting of sports events on VR/AR platforms

To extend the proposed system for real-time broadcasting of sports events on VR/AR platforms, several key steps can be taken:

Real-time Data Processing: Implement a real-time data processing pipeline to handle the continuous stream of data from the sensors. This would involve optimizing algorithms for efficiency and speed to ensure minimal latency in processing player tracking and pose estimation data.

Streaming Integration: Integrate the system with streaming technologies to broadcast the virtual sports events in real-time. This would involve setting up the infrastructure to stream the VR/AR content to viewers on different platforms.

Interactive Viewing Experience: Enhance the VR/AR viewing experience by allowing viewers to interact with the virtual environment, switch between different camera angles, and choose specific players to follow during the game.

Scalability: Ensure that the system is scalable to handle a large number of viewers simultaneously accessing the VR/AR broadcast. This may involve cloud-based solutions for scalability and resource management.

User Interface Design: Develop an intuitive user interface for VR/AR devices that allows viewers to navigate the virtual sports environment easily and access additional information or statistics during the game.

By implementing these enhancements, the system can provide a seamless and immersive real-time broadcasting experience for sports events on VR/AR platforms.

What are the potential challenges and limitations of using unsupervised domain adaptation for 3D human pose estimation in diverse real-world sports scenarios

Using unsupervised domain adaptation for 3D human pose estimation in diverse real-world sports scenarios may face several challenges and limitations:

Domain Shift: Real-world sports scenarios can vary significantly in terms of lighting conditions, player movements, and camera angles. Adapting a model trained on synthetic data to real-world scenarios may not capture all the nuances of the actual environment, leading to domain shift issues.

Labeling Complexity: Annotating 3D human pose data in diverse sports scenarios can be challenging and time-consuming. Unsupervised domain adaptation relies on pseudo-labeling or synthetic data, which may not always accurately represent the complexities of real-world poses.

Generalization: The model trained through unsupervised domain adaptation may not generalize well to unseen sports scenarios or unexpected variations in player poses. This lack of generalization could limit the model's performance in novel situations.

Performance Trade-offs: Balancing the trade-offs between accuracy and computational efficiency in unsupervised domain adaptation can be tricky. Optimizing the model for real-time performance while maintaining high accuracy poses a significant challenge.

Evaluation Metrics: Assessing the effectiveness of unsupervised domain adaptation methods for 3D human pose estimation in sports scenarios requires robust evaluation metrics that can capture the nuances of pose accuracy and generalization.

Addressing these challenges will be crucial in leveraging unsupervised domain adaptation effectively for 3D human pose estimation in diverse real-world sports scenarios.

What other applications beyond sports visualization could benefit from the multimodal and multiview sensing and analysis capabilities demonstrated in this work

The multimodal and multiview sensing and analysis capabilities demonstrated in this work have applications beyond sports visualization, including:

Surveillance and Security: Implementing similar sensor fusion techniques in surveillance systems can enhance security monitoring in public spaces, airports, and critical infrastructure. The ability to track and analyze multiple objects in real-time can improve threat detection and response.

Healthcare and Rehabilitation: Applying the system's pose estimation algorithms in healthcare settings can aid in physical therapy and rehabilitation programs. Monitoring patients' movements and providing real-time feedback on posture and exercise techniques can enhance recovery outcomes.

Entertainment and Gaming: Integrating multimodal sensing for avatar modeling and visualization can enhance virtual reality gaming experiences. Creating realistic avatars and interactive environments based on real-world movements can elevate the immersion and engagement levels in gaming.

Retail and Marketing: Utilizing multiview sensors for customer tracking and behavior analysis in retail spaces can provide valuable insights for marketing and store layout optimization. Understanding customer movements and interactions can lead to personalized shopping experiences and improved sales strategies.

Industrial Automation: Implementing the system in industrial settings for object tracking and pose estimation can enhance automation processes and quality control. Monitoring and analyzing the movements of objects and workers in manufacturing environments can improve efficiency and safety.

By exploring these diverse applications, the system's capabilities can be leveraged across various industries to enhance operations, experiences, and outcomes.