You will get a people counting & footfall analytics system | Computer Vision
Project details
I will build a people counting and footfall analytics system that runs on your existing CCTV cameras - no new hardware required in most cases.
WHAT YOU GET
▸ Person detection and tracking using YOLO with persistent IDs across frames
▸ Entry and exit counting across lines or zones you define
▸ Hourly, daily and weekly footfall reports
▸ Dwell time measurement per zone (Standard and Advanced)
▸ Live dashboard showing current occupancy and today's traffic (Standard and Advanced)
▸ CSV export and database logging
▸ Full source code and setup documentation
WHAT IT MEASURES
Entry and exit counts · current occupancy · peak hours and quiet periods · dwell time per area · zone-to-zone movement patterns · repeat-visit indicators where camera coverage allows.
HOW IT WORKS
1. Send me a few minutes of footage from each camera plus a floor sketch
2. I define the counting lines and zones with you and confirm placement
3. I build and tune the detection and tracking pipeline for your lighting and angles
DEPLOYMENT
Runs on a standard PC or server, or on an NVIDIA Jetson device for fully on-premise processing where footage cannot leave the building. Works with RTSP streams from most IP cameras.
WHAT YOU GET
▸ Person detection and tracking using YOLO with persistent IDs across frames
▸ Entry and exit counting across lines or zones you define
▸ Hourly, daily and weekly footfall reports
▸ Dwell time measurement per zone (Standard and Advanced)
▸ Live dashboard showing current occupancy and today's traffic (Standard and Advanced)
▸ CSV export and database logging
▸ Full source code and setup documentation
WHAT IT MEASURES
Entry and exit counts · current occupancy · peak hours and quiet periods · dwell time per area · zone-to-zone movement patterns · repeat-visit indicators where camera coverage allows.
HOW IT WORKS
1. Send me a few minutes of footage from each camera plus a floor sketch
2. I define the counting lines and zones with you and confirm placement
3. I build and tune the detection and tracking pipeline for your lighting and angles
DEPLOYMENT
Runs on a standard PC or server, or on an NVIDIA Jetson device for fully on-premise processing where footage cannot leave the building. Works with RTSP streams from most IP cameras.
Machine Learning Tools
NumPy, NVIDIA AI Platform, OpenCV, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$249
|
Standard
$549
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 18 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 6 |
Number of Graphs/Charts | 3 | 3 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
About Haresh
Computer Vision Engineer | YOLO, OpenCV, PyTorch & Jetson Edge AI
Surat, India - 2:49 am local time
My focus is real-time video analytics deployed on edge devices: object detection and tracking
running on NVIDIA Jetson, wired into live dashboards and alerting so the output is actually
usable by a business.
WHAT I BUILD
▸ Object Detection & Tracking - YOLO (v8/v11), custom-trained models on your own dataset, multi-object tracking, zone-based counting and dwell-time measurement.
▸ Retail & Hospitality Video Analytics - footfall counting, occupancy tracking, queue monitoring, and customer flow analysis from existing CCTV feeds.
▸ Compliance & Safety Monitoring - hygiene compliance detection, PPE detection, restricted- zone alerts, with real-time notification via Telegram, webhooks or email.
▸ ANPR / Licence Plate Recognition - plate detection, OCR, and logging pipelines for access control, parking and vehicle tracking.
▸ Edge AI Deployment - NVIDIA Jetson Orin Nano, model optimisation, TensorRT conversion, camera zone configuration, and multi-camera deployment.
▸ Dataset & Model Work - image annotation, dataset preparation, training, fine-tuning and accuracy evaluation.
WHAT MAKES MY DELIVERY DIFFERENT
Most computer vision freelancers hand you a model and a script. I deliver the whole system: the detection pipeline, the API around it, the database it writes to, and the dashboard your team actually looks at. I have a full-stack engineering background, so you don't need to hire a second developer to make the CV output usable.
TECH STACK
Vision & ML: Python · OpenCV · YOLOv8/v11 · PyTorch · TensorFlow · Roboflow · CVAT
Edge & Deployment: NVIDIA Jetson Orin Nano · TensorRT · ONNX · Docker
Delivery layer: FastAPI · REST APIs · PostgreSQL · React dashboards · WebSocket streams
Alerting: Telegram Bot API · webhooks · email/SMS triggers
HOW I WORK
Tell me what cameras you have, what environment they're in, and what you need detected. I'll tell you honestly whether it's feasible, what accuracy is realistic, and how I would approach it, before you commit to anything.
Send me a sample frame or short clip, and I will come back with a concrete plan.
(for search): computer vision engineer, computer vision developer, YOLO expert, YOLOv8 object detection, YOLOv11, OpenCV developer, PyTorch developer, deep learning engineer, edge AI deployment, NVIDIA Jetson developer, Jetson Orin Nano, TensorRT optimisation, real-time video analytics, object detection and tracking, people counting system, footfall analytics, occupancy monitoring, queue monitoring, retail video analytics, CCTV AI monitoring, surveillance AI, hygiene compliance detection, PPE detection, ANPR, licence plate recognition, image annotation specialist, dataset preparation, model training and fine-tuning, image segmentation, image classification, OCR, custom dataset creation, computer vision API, FastAPI computer vision, video processing pipeline
Steps for completing your project
After purchasing the project, send requirements so Haresh can start the project.
Delivery time starts when Haresh receives requirements from you.
Haresh works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis