You will get a Real-Time AI Fire Detection System with Alerts
Project details
Secure your environment with a production-grade AI Fire Detection System.
I am a Computer Vision Engineer specializing in real-time video analytics and surveillance security. Unlike basic detection scripts that spam you with errors, I build robust pipelines designed to filter false alarms and provide actionable intelligence.
Why choose this project?
• Instant Detection: I utilize optimized YOLO-based models for low-latency detection in live video streams.
• Smart Alerts: The system includes a real-time alert mechanism (WhatsApp/Email) so you are notified immediately when an event occurs.
• False Positive Reduction: I implement temporal validation logic (checking multiple frames) to distinguish real fire from glitches or momentary flashes.
• Evidence Capture: The system automatically captures and saves pre/post-event video clips for your records.
I have experience delivering high-impact computer vision projects for global clients and can deploy this solution on local machines or cloud environments. Whether you are monitoring a warehouse, a kitchen, or an outdoor area, I will fine-tune the system to your specific camera angles and lighting.
I am a Computer Vision Engineer specializing in real-time video analytics and surveillance security. Unlike basic detection scripts that spam you with errors, I build robust pipelines designed to filter false alarms and provide actionable intelligence.
Why choose this project?
• Instant Detection: I utilize optimized YOLO-based models for low-latency detection in live video streams.
• Smart Alerts: The system includes a real-time alert mechanism (WhatsApp/Email) so you are notified immediately when an event occurs.
• False Positive Reduction: I implement temporal validation logic (checking multiple frames) to distinguish real fire from glitches or momentary flashes.
• Evidence Capture: The system automatically captures and saves pre/post-event video clips for your records.
I have experience delivering high-impact computer vision projects for global clients and can deploy this solution on local machines or cloud environments. Whether you are monitoring a warehouse, a kitchen, or an outdoor area, I will fine-tune the system to your specific camera angles and lighting.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Keras, MATLAB, MLflow, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$80
|
Standard
$450
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 0 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
About Muhammad Muneeb
Senior Computer Vision Engineer | Real-Time AI Video Analytics
Rawalpindi, Pakistan - 2:21 pm local time
I build real-time AI systems that turn live video into actionable intelligence, whether that’s detecting fire in a facility, flagging suspicious behavior in retail, or analyzing player movement in sports.
Most computer vision models work in a notebook.
Mine run in production, on edge devices, in the cloud, or across multi-camera systems.
I specialize in designing full end-to-end pipelines:
• Data strategy and annotation design
• Model selection, training and fine-tuning
• Latency optimization (real-time performance)
• Multi-object tracking and action recognition
• Edge deployment (Jetson, TensorRT)
• Cloud APIs and scalable architecture
• Monitoring and performance improvement
🔎 What I Build
🚨 Surveillance & Safety AI
Real-time fire detection, shoplifting detection, intrusion monitoring, license plate recognition, behavioral analysis systems.
⚽ Sports Analytics
Player tracking, action recognition, pose-based performance analysis, automated highlight tagging, multi-camera tracking pipelines.
🏭 Industrial Vision
Defect detection, visual inspection systems, OCR pipelines, automated monitoring.
⚙ Technical Foundation
Deep Learning: PyTorch, TensorFlow
Detection & Tracking: YOLO, DeepSORT, ByteTrack
Action Recognition: SlowFast, R(2+1)D
Optimization: TensorRT, ONNX, Edge deployment
Backend & APIs: FastAPI, Flask
Cloud: AWS, GCP
Deployment: Docker, GPU inference pipelines
I don’t just train models.
I design production-ready AI systems that remain accurate under poor lighting, occlusion, motion blur, and real-world constraints.
If you are building:
• A smart surveillance system
• A sports analytics product
• A real-time AI camera application
• An edge-deployed vision system
Send me a brief description of your use case, and I’ll outline how I would architect your solution.
Let’s build something that works outside the lab.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Muneeb can start the project.
Delivery time starts when Muhammad Muneeb receives requirements from you.
Muhammad Muneeb works on your project following the steps below.
Revisions may occur after the delivery date.
Environment & Stream Analysis
I will review your sample video or live RTSP stream to understand the lighting, angles, and potential false-positive sources (like stoves or reflections).
Model Configuration & Fine-Tuning
I will configure the YOLO object detection model. If you have custom data, I will fine-tune the weights to maximize accuracy for your specific environment.