Computer Vision Specialist
Weekly Rate: $10,000/week
Overview
The Computer Vision Specialist drives the development of pose estimation algorithms and real-time motion tracking systems. This role is critical for achieving the required accuracy in squat detection while handling complex scenarios like occlusion, varying lighting conditions, and multi-athlete tracking.
Key Responsibilities
Pose Estimation and Algorithm Development - Create and optimize pose estimation algorithms specifically designed for accurate squat detection. Implement 2D/3D keypoint detection systems that can identify and track body joint positions including hips, knees, and spine alignment throughout the squat movement.
Multi-Camera Systems and Calibration - Design and implement multi-camera calibration and synchronization systems that coordinate feeds from multiple angles. Develop depth estimation techniques using stereo vision to calculate accurate 3D positions from 2D camera inputs.
Motion Tracking and Temporal Analysis - Build sophisticated motion tracking systems that analyze movement patterns over time, detecting rep completion and form breakdown. Implement frame interpolation and smoothing algorithms to ensure jitter-free tracking even with variable frame rates.
Model Selection and Optimization - Evaluate and select optimal pose estimation models including MediaPipe, OpenPose, and custom CNNs for squat-specific detection. Optimize these models for real-time inference, achieving sub-200ms end-to-end processing latency on edge hardware.
Robustness and Environmental Adaptation - Develop advanced occlusion handling strategies that maintain tracking accuracy when athletes are partially blocked. Create lighting compensation and normalization techniques that ensure consistent performance across varying venue conditions from bright outdoor events to indoor facilities.
Multi-Athlete Tracking - Implement robust multi-athlete tracking and identification systems that can differentiate between competitors in crowded workout stations. Ensure the system maintains individual athlete tracking even when paths cross or athletes work in close proximity.
Accuracy Validation and Testing - Design and execute comprehensive validation protocols to ensure the system achieves >95% squat detection accuracy. Develop testing frameworks that cover edge cases, different body types, and various squat techniques.
Research and Innovation - Stay current with the latest computer vision techniques and research, evaluating emerging technologies for potential integration. Develop custom pose models specifically optimized for squat biomechanics and competition standards.
Pipeline Optimization - Optimize the entire computer vision pipeline for maximum throughput and efficiency. Identify and eliminate bottlenecks to ensure the system can process multiple video streams simultaneously without degradation.
Required Skills
Computer Vision Leadership and Innovation demonstrates extensive experience developing sophisticated pose estimation systems that perform reliably in challenging real-world conditions. They have tackled complex vision problems involving human motion tracking where accuracy and speed are critical success factors. Their experience includes troubleshooting pose detection failures in crowded environments and developing solutions that maintain precision under varying operational conditions.
Advanced Research Foundation and Adaptation shows deep theoretical understanding of cutting-edge computer vision techniques with ability to bridge academic research and practical applications. They stay current with research developments and can adapt emerging breakthroughs to solve specific sports technology challenges. Their track record includes innovative solutions to novel problems that require custom approaches beyond standard implementations.
Pose Estimation Algorithm Expertise covers comprehensive knowledge of MediaPipe, OpenPose, and state-of-the-art deep learning approaches for human pose detection. They understand the strengths and limitations of different algorithms, particularly in sports environments with rapid movements and occlusion challenges. Their experience includes customizing and optimizing these algorithms for specific biomechanical requirements and performance constraints.
Technical Implementation and Optimization combines strong proficiency in OpenCV, TensorFlow, and PyTorch with real-time video processing expertise that meets demanding performance requirements. They understand the implications of different implementation choices on system performance and can optimize algorithms for specific hardware platforms. Their mathematical and algorithmic foundation enables development of custom solutions when existing approaches cannot meet accuracy or latency requirements.
Phase Allocation
The Computer Vision Lead maintains full-time engagement through Alpha, Beta, and Gamma phases, reflecting the critical importance of vision algorithms to system success. During Delta phase, allocation reduces as core algorithms stabilize and focus shifts to optimization and edge cases. The Full Release phase maintains significant involvement for ongoing algorithm refinement, handling new scenarios, and supporting production deployments.
| Phase | Weekly Rate | Allocation | Duration |
|---|---|---|---|
| Alpha | $10,000/week | 100% | 10 weeks |
| Beta | $10,000/week | 100% | 12 weeks |
| Gamma | $10,000/week | 100% | 8 weeks |
| Delta | $7,500/week | 75% | 10 weeks |
| Full Release | $4,000/week | 40% | 12 weeks |
Deliverables
Pose Estimation Algorithms. Production-ready pose estimation algorithms specifically optimized for squat detection and validation, including customized models that accurately identify key anatomical points during dynamic movement. These algorithms form the core of the computer vision system, providing reliable athlete tracking under competition conditions with robust handling of various body types and movement speeds.
Multi-Camera Calibration System. Comprehensive calibration framework for synchronized multi-camera setups, including automated calibration procedures and real-time synchronization protocols. This system ensures accurate 3D reconstruction from multiple viewpoints, enabling precise depth estimation and occlusion mitigation while maintaining calibration stability throughout competition events.
Occlusion Handling Strategies. Advanced algorithms for maintaining tracking continuity when athletes are partially obscured by equipment or other competitors. These strategies include predictive modeling for temporary occlusions and seamless handoff between camera views, ensuring consistent rep counting even in crowded competition environments.
Performance Benchmarks. Detailed performance metrics and optimization reports demonstrating algorithm compliance with latency and accuracy requirements. These benchmarks validate system performance across various hardware configurations and provide baseline measurements for continuous improvement throughout the project lifecycle.
Algorithm Documentation. Comprehensive technical documentation covering algorithm design, implementation details, and parameter tuning guidelines. This documentation enables knowledge transfer to the development team and provides clear guidance for system maintenance and future enhancements.
Testing Protocols. Structured testing frameworks for validating computer vision performance across diverse scenarios, including edge cases and failure modes. These protocols establish clear procedures for regression testing, performance validation, and accuracy verification throughout development and deployment phases.
Research Reports. Technical analysis of state-of-the-art approaches in pose estimation and sports analytics, including comparative studies and feasibility assessments. These reports document technology selection rationale and provide insights into emerging techniques that could enhance system capabilities.
Model Optimization Strategies. Detailed optimization plans for achieving required performance on edge hardware, including quantization approaches, model pruning techniques, and hardware-specific optimizations. These strategies ensure algorithms can run efficiently on deployed hardware while maintaining accuracy requirements.
Success Criteria
Detection Accuracy Achievement. The system achieves greater than 95% accuracy in squat detection and validation across all competition scenarios, including correct identification of valid repetitions and rejection of incomplete movements. This accuracy level is maintained across diverse athlete populations and movement variations encountered in HYROX competitions.
Latency Performance Standards. Computer vision processing maintains sub-200ms end-to-end latency from image capture through pose estimation to result generation. This performance standard ensures real-time feedback for athletes and judges while supporting the system's overall responsiveness requirements.
Robust Occlusion Management. The system successfully handles partial occlusions that occur during normal competition, maintaining tracking continuity when athletes are temporarily obscured. Multi-camera fusion ensures at least one clear view is always available, with seamless handoff between cameras when primary views are blocked.
Multi-Athlete Tracking Capability. Algorithms successfully track and differentiate multiple athletes simultaneously within the camera field of view, maintaining individual identity assignments throughout their exercise sets. This capability supports efficient competition flow without requiring strict athlete isolation or positioning constraints.
Environmental Adaptability. The computer vision system performs reliably across varying lighting conditions encountered in different venues, from bright outdoor events to dimly lit indoor facilities. Adaptive algorithms compensate for shadows, reflections, and changing illumination without degradation in tracking quality.
Tracking Stability and Smoothness. Pose estimation maintains smooth, jitter-free tracking throughout athlete movements, providing stable skeletal models without erratic position jumps or tracking loss. This stability ensures accurate movement analysis and prevents false positive or negative rep counts due to tracking artifacts.