Pose Estimation (2D)

The pose estimation component will form the technical core of our proposed HYROX wall ball judging system, responsible for converting detected persons into precise skeletal models that will enable accurate squat depth analysis. Our proposal suggests building on the RTMPose framework, which will deliver real-time 2D pose estimation followed by 3D analysis to provide the spatial accuracy required for automated judging decisions.

2D Pose Estimation Architecture

The 2D pose estimation system serves as the foundation for all downstream analysis, extracting precise skeletal keypoints that enable accurate 3D reconstruction and squat depth validation. Our architecture prioritizes real-time performance while maintaining the accuracy standards essential for competitive judging applications.

RTMPose Implementation Strategy

RTMPose real-time pose estimation demonstration - View on GitHub

Our proposed system will employ the RTMPose-t (tiny) variant (see documentation), optimized for real-time performance while maintaining keypoint accuracy above 68.5% on standard validation data. The model will extract 17 anatomical keypoints with particular emphasis on lower-body joints critical for squat depth assessment: hips, knees, and ankles. Think of these keypoints as digital markers placed on important body landmarks that allow the computer to understand body position.

Competition-Specific Optimizations will include enhanced training on partial blocking scenarios common when athletes hold wall balls, specialized data preparation for various lighting conditions found in competition venues, and consistency improvements that will maintain pose stability across rapid movement sequences. These optimizations ensure the system works reliably in real competition environments, not just controlled laboratory settings.

Keypoint Confidence and Quality Assessment

Each detected keypoint will include a confidence score ranging from 0.0 to 1.0, with our proposed system maintaining minimum confidence thresholds of 0.8 for hip joints and 0.7 for knee joints to help ensure reliable squat depth calculations. When keypoint confidence falls below these thresholds, the system will gracefully degrade to multi-view fusion or filling in missing data from nearby frames. Think of confidence scores as the system's way of saying how sure it is about each body part location.

Advanced Quality Assessment algorithms will analyze pose geometric consistency, identifying and filtering anatomically impossible configurations that may result from detection errors or severe blocking. This quality control layer will help prevent erroneous pose estimates from propagating through the judging pipeline, essentially acting as a sanity check to ensure the detected poses look human and realistic.

3D Integration

For three-dimensional pose reconstruction and depth analysis, the 2D pose estimates would be processed by the dedicated 3D lifting system. This separation of concerns allows optimized processing for each stage while maintaining clean architectural boundaries between 2D detection and 3D reconstruction.

Performance Optimization

Meeting the stringent latency requirements of competitive judging demands careful optimization across all aspects of the pose detection pipeline. Performance optimizations ensure consistent real-time processing while maintaining the accuracy standards essential for reliable automated judging.

Real-Time Processing Pipeline

Our proposed pose estimation pipeline would target 25-30 FPS throughput on NVIDIA Jetson hardware through several optimization strategies. GPU memory management would minimize data transfer overhead, while model quantization to INT8 precision could reduce inference time by 35% without compromising keypoint accuracy.

Pipeline parallelism would enable overlapped execution of 2D pose estimation and 3D lifting stages, with careful memory management that could prevent bottlenecks during peak processing loads. The system would aim to maintain deterministic timing characteristics essential for consistent judging performance across all competition stations.

Alternative Pose Estimation Models

While RTMPose represents our primary recommendation, several alternative pose estimation models could be considered for the HYROX judging system, each with distinct advantages and trade-offs:

MediaPipe Pose

MediaPipe Pose Strengths include excellent mobile and edge device performance with optimized inference engines designed for resource-constrained environments. The framework provides 33 body keypoints offering detailed body mapping beyond the standard 17-point skeleton, potentially improving analysis granularity.

Apache 2.0 License allows commercial use without restrictions, simplifying deployment considerations.

MediaPipe Pose Limitations center on single-person pose estimation focus that may require additional person detection preprocessing. The system shows less robustness in challenging lighting conditions compared to RTMPose, potentially impacting performance in diverse venue environments.

Limited Customization Options for competition-specific scenarios may require additional development effort to adapt for HYROX requirements.

Best fit: Mobile deployment scenarios or edge computing with limited hardware resources.

OpenPose

OpenPose Strengths include a proven track record in computer vision research with extensive academic validation and real-world application experience. The framework provides multi-person pose detection without requiring person detection preprocessing, simplifying pipeline architecture.

Comprehensive Keypoint Detection includes face and hand landmarks beyond body pose, offering potential for detailed movement analysis.

OpenPose Limitations present significant commercial deployment barriers including $25,000 annual licensing royalties and explicit prohibition on sports applications. Higher computational requirements limit real-time performance capabilities compared to modern alternatives.

Older Architecture design may not incorporate recent advances in efficiency and accuracy optimization found in newer frameworks.

Best fit: Research and academic applications where licensing constraints are acceptable.

AlphaPose

AlphaPose Strengths center on excellent accuracy for multi-person scenarios with sophisticated occlusion handling capabilities. The framework demonstrates strong performance in crowded scenes with multiple athletes, making it potentially suitable for busy competition environments.

Regional Pose Refinement capabilities provide improved accuracy through iterative enhancement of initial pose estimates.

AlphaPose Limitations include commercial licensing requirements for production deployment, adding cost and legal complexity. Higher computational overhead compared to RTMPose may impact real-time performance requirements.

Complex Setup and configuration requirements could complicate deployment procedures, while limited edge device optimization may restrict deployment flexibility.

Best fit: High-accuracy applications where computational resources are abundant.

MoveNet (TensorFlow)

MoveNet Strengths include Apache 2.0 licensing enabling unrestricted commercial deployment without ongoing royalty obligations. Two variants (Lightning/Thunder) are optimized for different performance-accuracy trade-offs, allowing flexible deployment choices.

Strong TensorFlow Integration provides excellent ecosystem support with comprehensive deployment tools and community examples.

MoveNet Limitations include single-person pose estimation requiring additional detection pipeline development. The 17-keypoint model provides less anatomical detail than some alternatives, potentially limiting analysis depth.

Limited Customization Options for domain-specific requirements may necessitate additional development work, while performance lags behind RTMPose in challenging lighting and occlusion scenarios.

Best fit: TensorFlow-based deployment pipelines or applications requiring license simplicity.

Trade-off Analysis

Model	Accuracy	Performance	Licensing	Multi-Person	Edge Support
RTMPose	Excellent	Excellent	Free/Academic	Yes	Good
MediaPipe	Good	Excellent	Free	No*	Excellent
OpenPose	Good	Fair	Restricted	Yes	Fair
AlphaPose	Excellent	Fair	Commercial	Yes	Fair
MoveNet	Good	Good	Free	No*	Good

*Requires additional person detection pipeline

Our recommendation for RTMPose stems from its optimal balance of accuracy, real-time performance, and licensing flexibility, making it particularly well-suited for competitive sports applications requiring high precision and reliability.

Adaptive Quality Control

Our proposed dynamic processing adaptation would adjust pose estimation parameters based on scene complexity and detection quality. During periods of high occlusion or challenging lighting, the system could automatically increase temporal smoothing and relax confidence thresholds while maintaining overall accuracy requirements.

Intelligent fallback mechanisms would activate alternative processing paths when primary pose estimation encounters difficulties. These could include increased reliance on temporal interpolation, enhanced multi-view fusion weighting, and selective keypoint estimation focusing on joints most critical for squat depth analysis.

Competition Environment Adaptations

Occlusion Handling Strategies

Our proposed system would implement sophisticated occlusion handling specifically tailored to wall ball exercise patterns. Common occlusion scenarios would include wall balls temporarily blocking torso or arm keypoints, multiple athletes in close proximity during busy periods, and equipment or venue infrastructure creating partial obstructions.

Temporal pose interpolation would help maintain skeleton continuity during brief occlusions, while multi-view fusion would provide redundancy when primary camera views are compromised. The system could potentially maintain accurate hip and knee tracking even with up to 30% keypoint occlusion through intelligent geometric constraints and biomechanical motion models.

Lighting and Environmental Robustness

Advanced preprocessing algorithms would normalize image contrast and brightness across different venue lighting conditions, helping ensure consistent pose estimation performance from bright spotlight conditions to dimmer corner locations. Automatic exposure adjustment would maintain optimal keypoint detection sensitivity regardless of venue-specific lighting variations.

Our proposed model training dataset would include extensive coverage of competition-specific scenarios including various athlete clothing colors, different venue backgrounds, and diverse lighting configurations found across HYROX's global event portfolio. This comprehensive training approach could help ensure robust performance across all deployment environments.

Integration and Data Flow

Pipeline Integration Architecture

Pose estimation results would be packaged with comprehensive metadata including keypoint confidence scores, temporal consistency metrics, and multi-view fusion quality indicators. This rich metadata would enable downstream processing stages to make informed decisions about data reliability and processing strategies.

Seamless integration with the tracking system would help ensure pose estimates are correctly associated with individual athlete identities, maintaining consistency across frame sequences and camera views. The system could provide predictive pose estimates that would assist tracking algorithms in maintaining robust athlete identification during rapid movements.

Quality Monitoring and Diagnostics

Real-time performance monitoring would track pose estimation accuracy, processing latency, and keypoint detection reliability across all active camera streams. Diagnostic capabilities could identify potential issues such as camera miscalibration, lighting changes, or model performance degradation that might impact judging accuracy.

Automated quality assessment would generate alerts when pose estimation confidence drops below acceptable thresholds, enabling proactive intervention before judging accuracy is compromised. Performance telemetry could provide detailed insights into system behavior that would inform ongoing optimization and maintenance strategies.