# Computer Vision Assessment - Executive Summary

**Date:** November 17, 2025
**Prepared by:** Computer Vision Engineer

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## The Core Issue: Strategic Plan Has Unrealistic Assumptions

The STRATEGIC_ANALYSIS.md contains three architectural assumptions that need revision:

### 1. Real-Time Latency Target is Incorrect

| Assumption                                                               | Reality                                  | Impact                   |
| ------------------------------------------------------------------------ | ---------------------------------------- | ------------------------ |
| \<200ms E2E with server-side MediaPipe                                   | 250-350ms realistic                      | Must revise architecture |
| Capture (33ms) + Network (50ms) + Process (50ms) + Render (33ms) = 166ms | Network is 80-120ms real-world, NOT 50ms | Hidden dependency        |

**Root cause:** 50ms network latency is optimistic. Real-world WebSocket streaming over home broadband: 80-120ms typical.

**Solution:** **Hybrid client-side + server architecture**

- Primary path: WebGL TensorFlow.js (in-browser) = \<150ms
- Fallback path: Server-side MediaPipe = 250-350ms
- Result: \<200ms on modern devices, graceful degradation

**Action:** Revise Task 3 to use hybrid architecture. Week 1 Task 3 must include latency profiling with actual setup.

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### 2. Multi-Person is "Immediately Feasible" - NOT Without Architecture Work

| Statement                                                                      | Reality                                                  | Effort                  |
| ------------------------------------------------------------------------------ | -------------------------------------------------------- | ----------------------- |
| "Multi-person immediately feasible for team analysis"                          | Kinemotion assumes SINGLE person per video               | 2 weeks                 |
| Requires: Person ID tracking, temporal association, per-person phase detection | Needs Hungarian algorithm for frame-to-frame association | Add architectural layer |

**Action:** Create separate Task 3B for multi-person support. DO NOT include in Task 3 MVP. Keeps real-time delivery on track.

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### 3. Running Gait is Feasible BUT Has Critical Dependency

| Component                                      | Dependency                                   | Status             |
| ---------------------------------------------- | -------------------------------------------- | ------------------ |
| Landing classification (heel/midfoot/forefoot) | Requires high-confidence foot_index landmark | Blocked by Task 1  |
| Current ankle angle calculation                | Uses heel (static), not foot_index (toe)     | Must fix in Task 1 |
| Task 4 schedule                                | "Start Week 5" but Task 1 is only 2-3 days   | Tight but feasible |

**Action:** Ensure Task 1 (ankle fix) validates foot_index confidence before Task 4 starts. Minimal schedule impact.

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## Quick Assessment: What's Actually Achievable

### Real-Time (\<200ms End-to-End)

- **Status:** YES, achievable
- **How:** Client-side MediaPipe (TensorFlow.js in browser)
- **Effort:** 4 weeks (vs 3-4 weeks server-only)
- **Latency breakdown:** 100-200ms typical (capture 43ms + inference 35ms + render 16ms + network optimization 50ms)

### Multi-Person Detection

- **Status:** YES, achievable
- **How:** Add temporal tracking layer (Hungarian algorithm)
- **Effort:** 2 weeks extra
- **Recommendation:** Task 3B (post-MVP), not Task 3

### Running Gait Analysis

- **Status:** YES, achievable
- **How:** Gait cycle detection + landing classification
- **Effort:** 2-3 weeks
- **Dependency:** Task 1 (ankle fix) must validate foot_index first
- **Key metrics:** Ground contact time, cadence, stride length, landing pattern

### 3-Sport Platform by Month 2

- **Status:** YES, on track
- **Scope:** Drop Jump, CMJ, Running
- **Dependencies:** Task 1 → Task 4 chain

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## MediaPipe Robustness: No CV Blockers

### Official Performance (from MediaPipe docs)

| Metric               | Value                | Implication                     |
| -------------------- | -------------------- | ------------------------------- |
| Inference latency    | 25-50ms              | NOT bottleneck (network is)     |
| Multi-person support | ~10 people per frame | Practical: 4-6 for teams        |
| Accuracy (PCK@0.2)   | 95-97%               | Excellent for athletic analysis |
| Real-time capability | Works on phones      | Proven by Ochy, Movaia          |

### Sport-Specific Robustness

| Sport        | Challenge                 | Mitigation                                  |
| ------------ | ------------------------- | ------------------------------------------- |
| Drop Jump    | Extreme deceleration      | High confidence threshold (0.6)             |
| CMJ          | Triple extension tracking | Focus on ankle/knee/hip                     |
| Running      | Continuous motion blur    | Looser threshold (0.45), gait smoothing     |
| Multi-person | Occlusion                 | Stricter threshold (0.7), temporal tracking |

**Key finding:** No fundamental MediaPipe limitations. Robustness is tuning confidence thresholds per sport.

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## Recommended Changes to Strategic Plan

### Change 1: Task 3 Real-Time Architecture

**From:** Server-side FastAPI + WebSocket
**To:** Hybrid (client-side TensorFlow.js primary + server fallback)

**Why:** Achieves \<200ms on modern devices without sacrificing fallback support

**Impact:** +1 week complexity, better UX, enables monetization (premium for server features)

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### Change 2: Task 3B - Multi-Person Tracking (Optional/Post-MVP)

**New:** Separate task for multi-person detection + team analysis

- Temporal person association (Hungarian algorithm)
- Per-person phase detection
- Team comparison metrics

**Timing:** Week 6-8 (after Task 3 MVP complete)

**Why:** Keeps Task 3 real-time focused. Multi-person is nice-to-have, not must-have for launch.

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### Change 3: Task 4 Start Condition

**From:** "Start Week 5" (parallel with Task 3)
**To:** "Start Week 6" (after Task 1 validated)

**Why:** Running analysis depends on robust foot_index landmark tracking, which Task 1 fixes. Avoids mid-development blockers.

**Schedule impact:** Minimal (Task 1 is only 2-3 days anyway)

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## Performance Optimization for \<200ms

### Client-Side Path (TensorFlow.js)

- WebGL acceleration: 35ms inference
- Canvas resolution optimization (480p): -15ms
- Frame skipping with confidence: -20ms
- Jitter buffer reduction: -30ms
- **Result: 50-120ms latency**

### Server-Side Path (Fallback)

- GPU batching (4 frames): -10ms
- Model quantization (INT8): -5ms
- WebSocket connection pooling: -15ms
- **Result: 220-300ms latency**

### Network Optimization (Both)

- WebRTC video stream (vs WebSocket): -50-100ms
- Metric compression (delta encoding): -5-10ms
- Jitter buffer optimization: -30ms

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## Implementation Timeline (Revised)

### Sprint 2 (Weeks 4-5)

- **Task 3 MVP:** Client-side real-time (160ms E2E target)
- **Deliverable:** Working demo \<200ms on modern browser

### Sprint 3 (Weeks 6-7)

- **Task 3 (continued):** Server fallback, optimization
- **Task 4 (start):** Running gait analysis MVP
- **Deliverable:** 2-sport real-time platform

### Sprint 4+ (Week 8+)

- **Task 3B (optional):** Multi-person team analysis
- **Task 4 (continued):** Full running validation
- **Deliverable:** 3-sport platform, team features

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## Risk Mitigation: Top 3 Actions

1. **Week 1 Task 3: Latency Profiling**

   - Measure actual system vs assumptions
   - Decide client vs server based on data
   - Build latency profiler tool for ongoing monitoring

1. **Task 1 Completion Before Task 4 Start**

   - Validate foot_index landmark confidence
   - Prevents mid-development discovery of landing classification issues

1. **Separate Multi-Person as Task 3B**

   - Prevents scope creep on Task 3 real-time
   - Team features are optional, not blocking MVP

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## Confidence Level Assessment

| Component                    | Confidence   | Notes                                                 |
| ---------------------------- | ------------ | ----------------------------------------------------- |
| Real-time \<200ms achievable | HIGH (95%)   | Client-side path proven by competitors (Ochy, Movaia) |
| Multi-person feasible        | HIGH (90%)   | Requires standard temporal tracking, not novel        |
| Running gait detection works | HIGH (90%)   | Cycle detection is straightforward, well-researched   |
| MediaPipe robustness         | HIGH (95%)   | Proven in production at other companies               |
| Architecture fits timeline   | MEDIUM (75%) | Depends on Week 1 Task 3 profiling decision           |

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## Next Steps

1. **Review this assessment** with stakeholders
1. **Week 1 Task 3:** Implement latency profiler, validate architecture decision
1. **Revise sprint planning** to include Task 3B, adjust Task 4 start date
1. **Proceed with confidence** - no CV blockers detected

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**Full Assessment:** See CV_ASSESSMENT_REAL_TIME_MULTI_SPORT.md for detailed technical analysis
