g
ghaithaali

Ghaith Ale

@ghaithaali
4.9(26)

Level 2

Computer Vision and Systems Development

Emiratos Árabes Unidos
Inglés, Árabe
Parte de la información aparece en idioma inglés.
Sobre mí
Motivated by a strong curiosity for how machines process spatial and visual data. Core focus centers on the complete development cycle of computer vision systems—from building custom data engineering tools and training deep learning models to writing low-overhead C++ inference utilities. Fascinated by the challenge of taking networks out of isolated cloud environments and optimizing pipelines to run fluidly alongside real-world vehicle telemetry on resource-constrained edge hardware.... Lee más

Habilidades

g
ghaithaali
Ghaith Ale
USD 25/hora
desconectado • 
Tiempo medio de respuesta: 1 hora

Revisa mis servicios

Visión Artificial
I will do computer vision, yolo, object detection, services
4.8(15)
Visión Artificial
I will train optimize and deploy realtime computer vision systems
5.0(9)

¿Quieres trabajar por horas?

Dile a Ghaith Ale qué necesitas.

USD 25

/

hora

Porfolio

Experiencia laboral

Contractor

1 yr 4 mos

On-Device Android Architecture

May 2026 - Jun 20261 mo

Android Privacy Application: Built an on-device body-blurring tool to hide clothes using a custom segmentation model, achieving 27+ FPS on low-power mobile CPUs via XNNPACK acceleration and frame buffers.

C++ Inference Framework

Apr 2026 - May 20261 mo

• ONNXBenchmarking Utilities: Developed a low-overhead, multi-threaded C++ helper framework to evaluate models and streamline ONNX Runtime deployment workflows. • Hardware Provider Auditing: Programmed initialization routines that query model metadata to extract structural configurations and bind available execution providers (CPU, CUDA, TensorRT). • Profiling Pipelines: Built benchmarking workflows supporting deterministic latency evaluation for single-image inference and live video streams to isolate model throughput metrics. • Runtime Threading Pools: Implemented asynchronous producer-consumer threading loops and pinned-memory allocation schemas to reduce OS-level cache misses and I/O decoding bottlenecks.

Lead Perception Engineer

Jan 2025 - Mar 20261 yr 2 mos

• Automated Driver Assessment: Programmed temporal rule engines to evaluate driver traffic behaviors, specifically tracking lane-change execution safety, head-pose mirror checks, indicator usage cycles, and active compliance with traffic lights and regulatory road signs. • Behavioral Compliance Fusion: Synchronized real-time Traffic Sign Recognition (TSR) and multi-state Traffic Light Classification models with time-series CAN-bus vehicle telemetry (velocity, indicators, brake status) and HD-map spatial priors to grade traffic rule adherence. • Multi-Camera Model Training: Trained and deployed distinct deep learning models optimized for specific camera placements, separating interior cabin-monitoring tasks from exterior surrounding-area perception updates. • Geometric Monocular Modeling: Combined deep learning perception outputs with camera intrinsics and horizon stability checks to execute 3D ground-plane object distance estimation (X,Z) assuming flat-ground geometry. • System Architecture & Memory Optimization: Deployed a split-compute edge network across a 10-vehicle fleet via VPN, implementing a zero-allocation host memory pipeline to eliminate frame accumulation latency (6×21 FPS per vehicle). Engineered an end-to-end custom GStreamer plugins; developed low-overhead C++ meta-data parsing modules to inject synchronized CAN-bus telemetry directly into the inference stream.

26 Reseñas
4.9

(25)
(1)
(0)
(0)
(0)
Desglose de calificaciones
  • Nivel de comunicación del Freelancer
    5
  • Calidad de la entrega
    4.9
  • Valor de la entrega
    4.8
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