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Home Projects Monitoring system with AI
falling detection system $150 Original price was: $150.$80Current price is: $80.
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Safety Monitoring System $150 Original price was: $150.$80Current price is: $80.

Monitoring system with AI

$100 Original price was: $100.$50Current price is: $50.

. Real-time fall detection system using YOLO custom models model (`best.pt’).
. Processes input from video file (`demo.mp4`) or webcam.
. Detects people using YOLO object detection and tracking.
. Uses aspect ratio (height/width) to identify fallen persons.
. Triggers alert when a fall is detected (red bounding box and label).
. Optimized processing by skipping frames for better performance.
. Displays real-time FPS counter and fall count on screen.
. Saves processed output video (`demo_fall_detected.mp4`).
. Shows live detection window using OpenCV.
. Uses lightweight rule-based logic without a separate ML classifier.

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Category: Projects Tags: AI, AI Projects, AI Surveillance, Artificial Intelligence, Camera, Computer Vision, DeepLearning, Detection, SmartSurveillance, video analytics, YOLO12
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Description
AI Security
Computer Vision
Custom models

Monitoring system with AI

Real-time surveillance using YOLO object detection and geometric analysis — automatically identifying falls in homes, hospitals, and industrial facilities without heavy computation.

● 5 min read
● Python · OpenCV
● Elderly Care · Safety Systems

This system watches a live video feed, locates every person in the frame, and checks their body proportions. A standing person is tall and narrow — a fallen person is wide and flat. No complex pose estimation required.
How the detection logic works
🧍
Standing — Normal
Bounding box height > width → person is upright. Green box, no alert triggered.
🛌
Fallen — Alert!
Bounding box width > height → person is horizontal. Red box + “FALLEN!” label + terminal alert.

Processing pipeline
Video / Webcam

›

Frame Skip

›

Object Detection

›

Person Tracking

›

Aspect Ratio Check

›

Alert + Annotate

Extendable alert integrations
🔊

Sound AlertLocal audio trigger on fall event

📱

SMS / PushTwilio or Firebase integration

✈️

Telegram BotInstant photo + message to caregiver

🌐

Web DashboardFlask / React live monitoring panel

Real-world use cases
01
Elderly CareDetect falls in homes and hospital rooms without wearable devices
02
CCTV SurveillanceIdentify accidents in public spaces and trigger automated responses
03
Industrial SafetyMonitor factory workers and alert supervisors to on-site accidents
04
Smart SecurityFully automated monitoring without requiring a human operator
Technical benefits
  • 01
    Real-time processingFrame skipping + fast inference keeps the system running smoothly even on standard laptops with no GPU required.
  • 02
    Lightweight fall logicGeometric aspect-ratio analysis replaces heavy pose estimation models — faster deployment, lower resource cost.
  • 03
    Scalable & customizableSwap in a custom model (best.pt), add any alert channel, or embed into a Flask web dashboard with minimal changes.
Tech stack
Python 3.x

OpenCV
Object Detection
Persistent ID Tracking
Geometric Analysis

Want to deploy this system?
The full source code, model weights, and integration guides are available below.

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