🛡️ Real-Time Person & Elderly Fall Detection System
Advanced Occlusion Resilience, Kinematic State Machine & Edge AI Architecture
An enterprise-grade, edge-compatible AI computer vision system designed to detect accidental falls in real-time under severe visual occlusions (behind furniture, tables, beds, couches) while completely eliminating false positives from daily activities such as sitting down, tying shoelaces, or sleeping in bed.
🌟 Key Highlights
- ⚡ Instant-Trigger Fall Detection: Detects human falls the millisecond a rapid descent ($v_y > 0.7\text{ h/s}$) or posture collapse ($\Delta \theta / \Delta t > 35^\circ/\text{s}$) occurs, triggering immediate visual and dispatch alerts.
- 🛏️ Smart Resting / Sleeping False-Alarm Rejection: Biomechanically differentiates between an accidental fall and peaceful horizontal resting/sleeping on a bed (
MONITORING (RESTING) in calm green, zero false sirens).
- 👁️ 4-Tier Occlusion Resilience:
- Kinematic Bone Constraint Imputation: Mathematically reconstructs blocked knees and ankles using anatomical anthropometric ratios.
- Temporal Velocity Extrapolation: Carries momentum vectors through visual blockages.
- Furniture ROI Tracking: Monitors trajectories descending behind known occlusion barriers (coffee tables, beds, sofas).
- Proximity & Bone Length Clamping: Enforces strict skeletal bounds to eliminate spurious spiderweb lines.
- 🚀 100% Edge CPU Ready: Powered by a lightweight pose estimation engine (~6.5 MB). Runs at full speed on standard CPUs without requiring a dedicated GPU or custom model training.
- 🔄 Continuous Interactive Runner: One-click launcher with auto-looping menu, video auto-discovery, and drag-and-drop support.
🚀 Quickstart Guide (1 Command to Run)
You only need ONE command:
python run.py
*(Or on Windows, simply double-click run.bat)*
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🛡️ AI PERSON & ELDERLY FALL DETECTION SYSTEM
Advanced Occlusion Resilience & Kinematic Tracking
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Select an input option:
[1] 📹 Live Webcam (Real-time monitoring)
[2] 📁 Custom Video File (Auto-detects videos + Drag & Drop)
[3] 🧪 Synthetic Occlusion Fall Demo (Built-in test)
[4] ❌ Exit
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- Option [1] (Webcam): Streams live camera feed with real-time skeleton tracking and HUD telemetry.
- Option [2] (Custom Video): Automatically lists all available test videos and benchmark clips with one-key selection (
1, 2, 3, 4, 5), or accepts any drag-and-dropped .mp4 / .avi file.
- Option [3] (Synthetic Demo): Generates and executes a synthetic scenario of a person walking and falling behind an occlusion barrier.
- Option [4] (Exit): Safely closes the application.
📦 Included Benchmark Video Suite (sample_videos/)
The repository includes real-world benchmark fall test videos:
| # |
Video File |
Scenario & Purpose |
Expected Result |
| 1 |
sample_videos/standing_fall_1.mp4 |
🏃 Sudden collapse fall from standing |
Instant [ALERT] FALL DETECTED |
| 2 |
sample_videos/standing_fall_2.mp4 |
🍌 Slip & backward fall from standing |
Instant [ALERT] FALL DETECTED |
| 3 |
sample_videos/sitting_to_fall.mp4 |
🪑 Senior citizen falling off chair |
Instant [ALERT] FALL DETECTED |
| 4 |
sample_videos/bed_rollout_fall.mp4 |
🛏️ Roll-out fall from bed onto floor |
Instant [ALERT] FALL DETECTED |
| 5 |
sample_videos/normal_activity_no_fall.mp4 |
🚶 Walking, sitting & sleeping (ADL) |
MONITORING (RESTING) (Zero False Alarms) |
🧠 Technical Architecture & Biomechanical Pipeline
┌─────────────────┐ ┌──────────────────────┐ ┌────────────────────────┐
│ Video / Webcam │ ──> │ YOLOv8-Pose Backbone │ ──> │ Occlusion Resilience │
│ Stream │ │ Keypoint Extractor │ │ & Bone Imputation │
└─────────────────┘ └──────────────────────┘ └────────────────────────┘
│
┌─────────────────┐ ┌──────────────────────┐ ▼
│ HUD Visualizer │ <── │ Multi-Stage Fall FSM │ <── ┌────────────────────────┐
│ & Alert Engine │ │ State Transitions │ │ Kinematic Extractor │
└─────────────────┘ └──────────────────────┘ │ (v_y, Angle, ω, Energy)│
└────────────────────────┘
1. Biomechanical Features
- Torso Angle ($\theta_{torso}$): Angle formed by the midpoint of shoulders to midpoint of hips relative to the floor horizontal ($90^\circ = \text{standing}, 0^\circ = \text{flat on ground}$).
- Vertical Descent Rate ($v_y$): Normalized downward velocity measured in subject bounding box heights per second ($\text{h/s}$).
- Angular Collapse Rate ($\omega = \frac{d\theta}{dt}$): Rate of postural angle drop in degrees per second.
- Stillness Energy ($E_{motion}$): Temporal variance across visible anatomical landmarks to confirm post-impact immobility.
2. State Machine Logic Flow
[ MONITORING ] ──( v_y > 0.7 h/s OR ω > 35°/s )──> [ FALL_DETECTED ]
▲ │
│ ( > 1.5s Stillness )
( Stands Up ) │
│ ▼
[ MONITORING ] <──────( Upright Motion )─────── [ CONFIRMED_FALL ]
📁 Repository File Structure
d:\A New Fall Detecion Sytem\
├── run.bat # One-click Windows desktop launcher
├── run.py # Interactive CLI runner with continuous loop
├── fall_detection_all_in_one.py # Standalone all-in-one pipeline (Zero sub-dependencies)
├── core/
│ ├── detector.py # YOLOv8-Pose wrapper & keypoint extractor
│ ├── tracker.py # Multi-person temporal tracklet manager
│ ├── occlusion.py # Occlusion engine, Kalman extrapolation & bone imputation
│ ├── kinematics.py # Biomechanical kinematic feature engine
│ ├── state_machine.py # Multi-stage Fall State Machine
│ └── visualizer.py # Telemetry HUD visualizer & alert banners
├── sample_videos/ # Benchmark fall test dataset
│ ├── standing_fall_1.mp4 # Sudden collapse fall
│ ├── standing_fall_2.mp4 # Slip and fall
│ ├── sitting_to_fall.mp4 # Chair fall
│ ├── bed_rollout_fall.mp4 # Bed roll-out fall
│ └── normal_activity_no_fall.mp4 # Normal ADL baseline (zero false alarm test)
├── test_generator.py # Synthetic occlusion test scenario generator
├── requirements.txt # Clean dependency manifest
├── FALL_DETECTION_SYSTEM_DESIGN.md # Comprehensive research & mathematical design spec
└── README.md # Documentation & quickstart guide
🛠️ Advanced CLI Usage (Direct Bypass)
If you prefer direct command-line execution without the interactive menu:
# Run on webcam (device 0)
python fall_detection_all_in_one.py --source 0 --show
# Run on a specific video file
python fall_detection_all_in_one.py --source sample_videos/standing_fall_1.mp4 --show --save output_result.mp4
# Run synthetic occlusion demo
python fall_detection_all_in_one.py --source demo --show
📜 Scientific Reference & Design Document
For full mathematical formulations, biomechanical formulas, Kalman filter state equations, and multi-view homography matrices, refer to:
👉 FALL_DETECTION_SYSTEM_DESIGN.md
这是一个边缘 AI 计算机视觉系统，可实时检测跌倒，尤其针对老年人，即使人被家具部分遮挡也能检测，同时避免把坐下或睡觉等正常活动误报为跌倒。