Computer Vision Opportunities

Computer vision technology can now analyze human motion in real-time with the precision needed for competitive sports judging. Modern pose estimation models can track skeletal keypoints at 30+ frames per second, identifying joint positions with sub-centimeter accuracy even in challenging visual conditions. This capability allows us to measure hip and knee angles continuously throughout each squat repetition, applying consistent mathematical criteria rather than subjective visual assessment. The technology has proven itself in medical rehabilitation, sports training, and fitness applications worldwide.

Other companies have proven this approach works in similar applications. Trifork's partnership with Rokoko Care showcases how computer vision can track rehabilitation exercises through smartphone cameras, achieving clinical-grade accuracy for movement assessment. The CoCo Care App allows physiotherapists to assign home exercise programs that patients complete while the app monitors form and counts repetitions automatically. This consumer-grade hardware approach—using standard cameras rather than specialized sensors—directly parallels our proposed HYROX implementation.

Sports organizations already use computer vision where precision and consistency matter. InData Labs reports that Human Pose Estimation technology was successfully implemented in CrossFit competitions, facilitating fast and accurate competition results through real-time tracking and analysis of athletes' movements. The technology tracks athletes' movements in real time, analyzes the correctness of physical exercise performance, and counts repetitions while indicating which exercises were performed properly or with errors. This proves our HYROX system can work in competitive fitness environments. Additionally, research from Infront Sports & Media, HYROX's investment partner, confirms the critical need for technological solutions to support the sport's explosive growth from 600 racers in 2018 to over 500,000 expected in 2025.

Our requirements match what computer vision can do today without pushing beyond proven capabilities. Our system needs to identify specific anatomical landmarks (hips, knees, shoulders), calculate relative positions, and compare measurements against defined thresholds—all well-established computer vision tasks. Unlike facial recognition or biometric identification that raise privacy concerns, our approach focuses solely on skeletal positioning that maintains athlete anonymity. This simplified scope increases reliability while reducing computational requirements, enabling deployment on edge devices rather than cloud infrastructure.

Camera placement needs to balance good angles with venue realities. The ideal position would capture athletes from the side at hip height, providing clear visibility of the hip-knee relationship during squats. However, this placement would position cameras directly in high-traffic areas where athletes move between stations, creating both safety hazards and equipment damage risks. Cameras cannot extend more than 10 centimeters into athlete pathways unless mounted above 3 meters height, eliminating ground-level tripod solutions that would provide optimal angles.

Different camera positions create different challenges that we need to solve through smart system design. Overhead mounting above 3 meters ensures equipment safety but creates steep viewing angles that complicate depth assessment. Front-facing positions avoid athlete interference but struggle to evaluate lateral hip position accurately. Rear mounting eliminates obstruction risks but cannot observe knee angles reliably. Our solution will likely require multiple camera angles with intelligent view synthesis, using redundancy to overcome individual perspective limitations.

Competition venues create tough conditions that standard computer vision systems don't expect. Lighting varies dramatically between venues and even within single competition spaces, from bright directional spotlights to dim corner areas. Background crowds create visual noise that can confuse person detection algorithms. Other athletes warming up or transitioning through frame edges trigger false positives that must be filtered. Sweat and chalk dust can obscure visual markers, while athlete clothing ranges from form-fitting to loose garments that hide body contours. These real-world complexities demand robust algorithms trained on diverse competition footage rather than laboratory data.

Poor internet at venues means we must process everything locally rather than relying on cloud systems. Competition venues often lack reliable internet connectivity, particularly in convention centers or temporary event spaces. Even when connections exist, streaming high-definition video from 40-80 stations would overwhelm typical venue bandwidth. The ≤200ms latency requirement for real-time feedback eliminates cloud processing even under ideal network conditions. Our system must therefore process video feeds locally, either at each station or through on-site servers positioned within 100 meters of the competition floor.

System Components

Our cameras will be industrial-grade units built to run continuously in tough environments. Each station will require 2-3 cameras to ensure adequate coverage from multiple angles, with specifications including minimum 1080p resolution at 30fps, wide dynamic range for varying light conditions, and IP64 weatherproofing for outdoor events. Camera synchronization ensures frames align temporally across multiple viewpoints, enabling accurate 3D position reconstruction. Power-over-Ethernet connectivity simplifies installation while providing reliable data transmission to processing units.

Local processing units will analyze video in real-time using specialized neural networks for pose detection. Each unit will process feeds from 4-8 wall ball stations, leveraging hardware acceleration through GPUs or specialized AI chips to achieve required latency targets. The processing pipeline includes person detection to identify active athletes, pose estimation to extract skeletal keypoints, depth calculation using multi-view geometry, and rule validation against HYROX standards. Load balancing across multiple units ensures system resilience if individual processors fail.

Our integration system will connect the computer vision to HYROX's current Digital Wall Ball Target setup. This requires developing APIs that communicate validation results to each target's display controller, maintaining synchronization between visual feedback and rep counting. The integration must support backward compatibility with current hardware while enabling future enhancements. Local network architecture using dedicated VLANs ensures reliable communication without interfering with other event systems like timing chips or broadcasting equipment.

Judges and tech staff will get a control dashboard to monitor and manage the system. This web-based dashboard displays system status for all stations, confidence scores for borderline decisions, and options for manual override when needed. The adaptive division requires special consideration, allowing judges to disable depth requirements for athletes with movement limitations. The interface also supports gender classification override and division assignment based on wristband detection, ensuring accurate categorization when automatic detection fails.

Technical Advantages

Processing everything on-site removes dependencies that could hurt event operations. By computing all video analysis on-site, our system remains fully functional regardless of internet connectivity, cloud service availability, or network congestion. This self-contained architecture ensures consistent performance across diverse global venues while maintaining data sovereignty in regions with strict privacy regulations. Athletes can compete knowing their performance data never leaves the venue without explicit consent.

Real-time feedback does more than just judge reps - it actively helps athletes perform better. Visual indicators can show proximity to depth thresholds, helping athletes calibrate their movement patterns during warmups. Audio cues can alert athletes when they're approaching failure zones, allowing form correction before reps are invalidated. This immediate feedback loop transforms judging from a punitive function to a performance enhancement tool, improving both athlete satisfaction and movement quality.

Capturing detailed data creates analytics possibilities that human judges simply cannot provide. Beyond basic rep validation, our system can measure squat depth angles, rep cadence, power output estimates, fatigue indicators, and movement consistency metrics. This data enables post-event analysis for training optimization, real-time broadcast graphics showing performance metrics, and longitudinal tracking of athlete improvement across seasons. The information becomes a valuable asset for HYROX, athletes, and coaches seeking competitive advantages.

Software updates will keep the system current as competition requirements evolve over time. Unlike hardware-dependent solutions, our computer vision algorithms can be updated remotely to accommodate rule changes, add new movement standards, or optimize for different exercises. If HYROX introduces new workout stations or modifies existing standards, software updates can adapt the system without replacing physical infrastructure. This flexibility ensures the solution remains valuable throughout HYROX's continued evolution.

Integration Benefits

Our system will work with current infrastructure to minimize disruption during rollout. Our system augments rather than replaces the current Digital Wall Ball Target setup, preserving previous investments while adding new capabilities. Judges can continue using familiar interfaces with enhanced information displays, reducing training requirements and adoption resistance. The phased rollout approach allows gradual integration, starting with pilot events before expanding system-wide.

Efficiency gains go beyond using fewer judges - they improve entire event operations. Automated judging reduces pre-event judge briefing time, eliminates judge rotation schedules, and simplifies dispute resolution through video replay. Technical staff can monitor all stations from centralized positions rather than circulating continuously. Setup procedures become more straightforward without complex judge positioning requirements. These cumulative efficiencies reduce event operational costs while improving overall execution quality.

Better athlete experience will grow participation through improved competition fairness. Athletes gain confidence knowing their performance will be judged consistently regardless of event location, time of day, or judge assignment. The immediate feedback helps athletes optimize their technique during competition rather than discovering form issues through post-event video review. Detailed performance data provides training insights that help athletes prepare more effectively for future events. These improvements in fairness, feedback, and insights create compelling value propositions that attract new participants while retaining existing athletes.