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 & AI Engineer | Edge AI, VLMs & Spatial AI
Rawalpindi, Pakistan - 11:52 am local time
I don’t just train bounding-box detectors. I architect production-grade AI, Multi-Object Tracking, and Agentic Vision systems that turn raw, messy video streams into autonomous business intelligence.
Worked as a Computer Vision & Machine Learning Engineer for a UK-based sports analytics company (HITAI), I build real-time video pipelines that handle severe occlusion, poor lighting, motion blur, and edge-computing latency constraints daily.
Also Worked as a Head of AI Software Development for a UK corporate group (TADGT Group), I led the architectural design and go-to-market delivery of commercial AI products, including real-time retail surveillance and automated loss-prevention systems.
Whether you need to map athlete coordinates to a 2D/3D court, automate loss prevention in retail, or connect Vision-Language Models (VLMs) to autonomous alerting workflows, I build systems that work outside the lab.
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🔥 CORE DOMAINS & READY-TO-DEPLOY SOLUTIONS
⚽ Sports Biomechanics & Spatial Analytics
• Built broadcast sports pipelines (Padel, Basketball, Combat Sports) using YOLOv8 + ByteTrack for multi-player tracking and custom YOLO-Pose (13–17 point skeletal pose estimation).
• Specializing in TrackNet ball tracking, homographic 2D court projection, velocity curves, and positional heatmaps.
🛡️ Autonomous Surveillance, Loss Prevention & Retail AI
• Developed real-time Shoplifting & Intrusion Detection pipelines with custom polygon zones, WebSocket alerts, and pre/post-incident video buffering.
• Built end-to-end Gun Detection systems integrated with Gemini-powered AI Agents that autonomously describe scenes, ground camera locations, and trigger dispatch protocols.
🚗 Traffic, ANPR & Industrial Vision
• Automatic License Plate Recognition (ALPR) using YOLOv8 + EasyOCR + SORT tracking.
• Real-time traffic density, driving violation detection, and automated defect-inspection APIs.
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🛠️ TECHNICAL STACK
• Perception & Tracking: YOLO (v8/v11), ByteTrack, DeepSORT, MediaPipe, Open3D, PyTorch3D
• Action & Biomechanics: SlowFast, Custom Pose Estimation, Spatial Homography
• GenAI & Agentic AI: VLM Integration (Gemini, GPT-4o), LLM Tool-Calling, Automated Decision Loops
• Production Backend: FastAPI, WebSockets, RTSP/Live CCTV Pipelines, OpenCV, Python
• Infrastructure & Edge: Docker, TensorRT, ONNX, Cloud Inference (AWS, GCP)
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💡 HOW WE WORK TOGETHER
1. Architectural Review: You send me your use case, camera specs, or sample footage.
2. Direct Assessment: I evaluate latency budgets, lighting constraints, and deployment targets (Edge vs. Cloud).
3. Production Delivery: You get clean, modular, well-documented code with structured JSON/API outputs, not a research experiment.
If you are building an intelligent camera system, a sports analytics tool, or an autonomous video monitoring platform, send me a message with a brief description of your project. Let's scope out your architecture today.
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.