You will get advanced CCTV and video analytics app
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Project details
๐๐-๐๐จ๐ฐ๐๐ซ๐๐ ๐๐๐๐ & ๐๐ข๐๐๐จ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ: ๐๐ฎ๐ข๐ฅ๐ญ ๐๐ฒ ๐๐ง๐ ๐ข๐ง๐๐๐ซ ๐๐๐ก๐ข๐ง๐ ๐๐ & ๐๐จ๐จ๐ ๐ฅ๐-๐๐๐๐ค๐๐ ๐๐ญ๐๐ซ๐ญ๐ฎ๐ฉ๐ฌ ๐
๐+ ๐ฒ๐๐๐ซ๐ฌ ๐ฌ๐ก๐ข๐ฉ๐ฉ๐ข๐ง๐ ๐๐/๐๐ ๐๐จ๐ซ ๐๐ & ๐๐จ๐จ๐ ๐ฅ๐-๐๐๐๐ค๐๐ ๐ฌ๐ญ๐๐ซ๐ญ๐ฎ๐ฉ๐ฌ. I turn your CCTV/IP feeds into real-time, actionable intelligence.
๐ ๐๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง๐ฌ
โข People/vehicle counting, queues & zones
โข Tracking (ByteTrack), dwell/flow
โข ANPR (plate recognition)
โข PPE, intrusion, fall detection
โข Smart traffic & incident alerts
โข Retail loss prevention, robotic vision
โ๏ธ ๐๐๐๐ก
โข Models: YOLOv8/v9, RT-DETR, SAM 2.1, DINOv3
โข Frameworks: PyTorch, OpenCV, ONNX, TensorRT
โข Deploy: FastAPI, Docker, Nvidia Triton Inference Server, TorchServe
โข Edge/Mobile: TFLite, CoreML, Jetson
๐ฆ From pilot to scale: design, customize, and deploy endโtoโend pipelines (realโtime + postโprocessing).
๐๐๐ฌ๐ฌ๐๐ ๐ ๐ฆ๐ ๐ญ๐จ ๐ฌ๐ญ๐๐ซ๐ญ ๐ฒ๐จ๐ฎ๐ซ ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ ๐ฏ๐ข๐๐๐จ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐ฉ๐ข๐ฉ๐๐ฅ๐ข๐ง๐ ๐ญ๐จ๐๐๐ฒ.
๐+ ๐ฒ๐๐๐ซ๐ฌ ๐ฌ๐ก๐ข๐ฉ๐ฉ๐ข๐ง๐ ๐๐/๐๐ ๐๐จ๐ซ ๐๐ & ๐๐จ๐จ๐ ๐ฅ๐-๐๐๐๐ค๐๐ ๐ฌ๐ญ๐๐ซ๐ญ๐ฎ๐ฉ๐ฌ. I turn your CCTV/IP feeds into real-time, actionable intelligence.
๐ ๐๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง๐ฌ
โข People/vehicle counting, queues & zones
โข Tracking (ByteTrack), dwell/flow
โข ANPR (plate recognition)
โข PPE, intrusion, fall detection
โข Smart traffic & incident alerts
โข Retail loss prevention, robotic vision
โ๏ธ ๐๐๐๐ก
โข Models: YOLOv8/v9, RT-DETR, SAM 2.1, DINOv3
โข Frameworks: PyTorch, OpenCV, ONNX, TensorRT
โข Deploy: FastAPI, Docker, Nvidia Triton Inference Server, TorchServe
โข Edge/Mobile: TFLite, CoreML, Jetson
๐ฆ From pilot to scale: design, customize, and deploy endโtoโend pipelines (realโtime + postโprocessing).
๐๐๐ฌ๐ฌ๐๐ ๐ ๐ฆ๐ ๐ญ๐จ ๐ฌ๐ญ๐๐ซ๐ญ ๐ฒ๐จ๐ฎ๐ซ ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ ๐ฏ๐ข๐๐๐จ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐ฉ๐ข๐ฉ๐๐ฅ๐ข๐ง๐ ๐ญ๐จ๐๐๐ฒ.
Machine Learning Tools
Amazon SageMaker, ChatGPT, MLflow, NumPy, Open Neural Network Exchange, OpenCV, Python, PyTorch, Tesseract OCR, Vertex AIWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$6,000
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
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About Muhammad Abdullah
Senior Computer Vision & ML Consultant | Triton | PyTorch | Postgres
100%
Job Success
Rawalpindi, Pakistanย - 7:18 pm local time
๐๐จ๐ญ๐๐ก๐ ๐๐ฏ๐๐ซ๐๐ ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐๐ซ ๐๐ข๐ฌ๐ข๐จ๐ง ๐๐ฎ๐ฒ ๐
I donโt stop at a cool demo. Shipping LootMart (hyper-local marketplace) taught me the full stack around models: clean APIs, rock-solid data contracts, observability, security, and predictable costs. Thatโs why my CV/ML services behave like products, not science projects.
๐๐ก๐๐ญ ๐ ๐๐๐ญ๐ฎ๐๐ฅ๐ฅ๐ฒ ๐๐จ
- Computer Vision & Video Analytics (2D/3D): detection (YOLO/DETR), multi-object tracking (ByteTrack/DeepSORT), segmentation (U-Net), OCR/document AI, pose/re-ID, visual search & face/product matching (Siamese + Triplet Loss), point clouds & geometry.
- High-Throughput Inference: NVIDIA Triton (dynamic batching, concurrent models, HTTP/gRPC), TensorRT (FP16), ONNX Runtime; autoscaling containers with health checks and graceful rollouts.
- Robust Ingestion: multi-RTSP pipelines with back-pressure control using OpenCV, FFmpeg, PyAV/decord so frames donโt mysteriously vanish under load.
- MLOps & Services: FastAPI/Flask gateways, worker queues, CI/CD, Docker + Nginx; W&B for experiments; versioned datasets; reproducible training.
- Data & Integrations: Postgres (schema design, RLS, SQL/PLpgSQL), Redis, vector DBs (Milvus/Qdrant), webhook-driven architectures, n8n workflows for ETL/alerts, and MCP (Model Context Protocol) to wire AI tools into your internal systems.
- Selective Full-Stack Glue (when it helps): Next.js app layers, secure webhooks, auth, real-time updates, and crisp dashboards so stakeholders can see impact.
๐๐ซ๐จ๐จ๐ ๐ข๐ง ๐ญ๐ก๐ ๐๐ฎ๐๐๐ข๐ง๐ (๐๐๐๐๐ง๐ญ ๐๐ข๐ง๐ฌ)
1. Triton-backed real-time CCTV analytics across multiple cameras on commodity GPUs (dynamic batching = buttery latency).
2. Visual matching pipelines (Siamese/Triplet) for search/dedupe with rigorous evals and W&B tracking.
3. Heavy research models โ ONNX/TensorRT โ low-latency services that actually survive production traffic.
4. Production plumbing that lasts: Postgres-first data contracts, webhook fan-out, n8n automations... no brittle glue.
๐๐จ๐ฐ ๐๐โ๐ฅ๐ฅ ๐๐จ๐ซ๐ค (๐๐๐ ๐ ๐ข๐ซ๐ฌ๐ญ, ๐๐ฅ๐ฐ๐๐ฒ๐ฌ)
1. 30-min discovery โ lock in the KPI (latency, accuracy, throughput, cost).
2. Roadmap & estimate โ phases, risks, acceptance tests.
3. Build & validate โ baselines first, then iterate; measurable deltas each milestone.
4. Handoff & scale โ docs, runbooks, and knowledge transfer so your team owns it.
๐๐จ๐ซ๐ ๐๐ญ๐๐๐ค
Python โข PyTorch โข TensorRT โข ONNX Runtime โข NVIDIA Triton โข OpenCV โข Kornia โข FFmpeg โข PyAV/decord โข Postgres โข Redis โข Milvus/Qdrant โข FastAPI/Flask โข Next.js โข Docker โข Nginx โข Weights & Biases โข Webhooks โข n8n โข MCP
๐๐ฏ๐๐ข๐ฅ๐๐๐ข๐ฅ๐ข๐ญ๐ฒ
Consulting/part-time (fractional) engagements: architecture reviews, performance tuning, prototypes, or owning a CV/ML workstream. Top-rated on Upwork. Minimum $100/hr.
If you want production-ready computer vision, real-time video, reliable pipelines, and clear ROI, ๐ฅ๐๐ญโ๐ฌ ๐ญ๐๐ฅ๐ค. Iโll map your goal to a pragmatic plan and ship results you can measure.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Abdullah can start the project.
Delivery time starts when Muhammad Abdullah receives requirements from you.
Muhammad Abdullah works on your project following the steps below.
Revisions may occur after the delivery date.
Finalize the scope of work, timeline and budget via chat.
This helps us share a mutual understanding of what needs to be done as we move forward.
Write a proposal to be approved by you based on our discussion.
The proposal, once approved, marks our commitment and agreement.



