AI Vision Models for Smart Padel Courts

@MuhammadUzair16

Hi I am an embedded AI systems engineer with over 16 years of experience. I have extensive experience building real-time computer vision pipelines with Python/OpenCV, YOLO-class detection, multi-object tracking, pose estimation, camera calibration/homography, and deployment on NVIDIA Jetson using CUDA/TensorRT, RTSP and GStreamer. For your smart padel court system, I would start by validating the two-camera geometry and calibration quality, then build the detection -> tracking chain with identity stabilization across occlusion/crossovers, smoothing and confidence handling so the downstream stats are not driven by noisy tracks. The court mapping would convert image tracks into real court coordinates, followed by movement analytics such as heatmaps, distance, zone-time and side/player stats. Highlight extraction can then be generated from pose triggers and motion events into a documented per-match package: JSON, clips and visual outputs. Relevant CV/ML projects I have worked on include real-time object/player tracking, embedded vision systems, camera calibration and production-grade Linux deployments; some client projects cannot be shared publicly, but I can discuss/show suitable examples privately after shortlisting. A few useful details to confirm would be camera placement, resolution/FPS, sample match footage, whether any annotated data exists, and the expected real-time latency on the Jetson. Please contact me to discuss details.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@indiasofttel

Hello Dear Client, My area of expertise lies in designing efficient data processing workflows, optimizing performance, and delivering reliable, maintainable solutions. I have over 12 years of experience in developing the mobile ,web and desktop application whith AI/ML engagement.I have also strong hands on Python,TF,Pytorch,Rag,Vector DB .I wuld be happy to discuss and I can move on. Regards

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@malika335

Hi, I can build the complete computer vision pipeline for your smart padel courts with a focus on accuracy, performance, and Jetson deployment. **My approach:** * YOLO-based player detection with robust multi-object tracking (ByteTrack/BoT-SORT). * Stable player identities during occlusions and crossovers. * OpenCV homography for accurate real-world court mapping. * Pose estimation for gesture-based highlight triggers. * Movement analytics including heatmaps, distance, and zone statistics. * Clean JSON output and automated highlight clip generation. **Why work with me?** * Strong experience building AI and computer vision pipelines. * Skilled in Python, OpenCV, and real-time video processing. * Comfortable with SSH, Linux, RTSP/GStreamer, and Jetson optimization. * Clean, well-documented, and scalable code with regular progress updates. I'm available to start immediately and would be happy to discuss the technical details. Best regards, Ahmad Hassan https://www.freelancer.com/u/malika335

@devsrank

Hi, We would like to grab this opportunity and will work till you get 100% satisfied with our work. We are an expert team which have many years of experience on Java, Python, Matlab and Mathematica, Algorithm, Machine Learning (ML), C++ Programming, OpenCV, Computer Vision Please come over chat and discuss your requirement in a detailed way. Thank You

@mayankinnovative

Hello, I reviewed your AI Vision Models for Smart Padel Courts project and it immediately stood out because real-time computer vision can transform padel courts with automated analytics, player tracking, and match insights. AI-based padel systems commonly use computer vision for ball tracking, player detection, event recognition, and performance analysis. Problem: Building a real-time vision system requires accurate ball tracking, player detection, event recognition, and low-latency processing under varying lighting conditions. Solution: I can develop AI vision models for player and ball tracking, court detection, shot/event recognition, pose estimation, and real-time analytics with optimized deployment. My experience includes Python, OpenCV, YOLO, TensorFlow, PyTorch, FastAPI, and computer vision. Result: You will receive a scalable, high-performance AI solution that delivers accurate analytics and a seamless smart court experience. A few questions: • Which features are required first—ball tracking, player tracking, line detection, or shot recognition? • Will the system run on edge devices (Jetson), cloud servers, or both? • How many cameras are installed per court, and what are their specifications? • Do you already have labeled training data, or should dataset preparation and model training be included? I can start immediately and provide regular progress updates throughout the project. Thanks

@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings

@datacode0023

I would be happy to help let's chat Hello, I have over 9 years of experience working on AI projects and have successfully contributed to multiple projects in this field. I also hold a Master's degree in Artificial Intelligence. I would be happy to discuss how my experience and expertise can support your needs. Please feel free to contact me to discuss further. Have a nice day.

@albertc1996

Your project to transform padel courts into smart arenas aligns perfectly with my 8 years of experience in Computer Vision and edge AI deployment. I specialize in building robust, real-time vision pipelines that convert raw video feeds into precise, actionable analytics. **Key Challenges & Technical Solutions** 1. **Identity Stability Under Occlusion:** Players frequently cross paths and block each other. Standard tracking often fails here. - *Solution:* I will implement a hybrid approach using YOLO for detection and DeepSORT or ByteTrack for association, enhanced with Re-ID features if needed. This ensures player IDs remain consistent even during short-term occlusions. 2. **Court Mapping Accuracy:** Mapping 2D video coordinates to real-world court positions requires precise calibration. - *Solution:* I will use OpenCV’s homography estimation. I’ll start with manual calibration using known court corners and add an auto-calibration layer that detects court lines to correct drift over time, ensuring accurate heatmap and distance calculations. 3. **Jetson Edge Constraints:** Running heavy models on NVIDIA Jetson without latency is tricky. - *Solution:* I will optimize models using TensorRT and FP16 precision. By batching processing and optimizing the GStreamer pipeline, we can maintain real-time performance (>25 FPS) while extracting high-fidelity pose and tracking data. **Proposed Roadmap** 1. **Data & Environment Setup:** Connect to the Jetson device, set up the Docker container with CUDA/TensorRT, and ingest sample RTSP streams. 2. **Detection & Tracking Core:** Deploy YOLO for player detection and integrate DeepSORT/ByteTrack for multi-object tracking. Validate identity stability across test clips. 3. **Court Homography & Calibration:** Implement OpenCV-based coordinate mapping. Validate accuracy by comparing tracked points against known court lines. 4. **Pose Estimation & Highlight Logic:** Add MediaPipe or BlazePose for gesture detection (e.g., T-pose for serves). Define trigger logic for highlight extraction based on game events. 5. **Analytics Output Generation:** Develop the script to produce JSON stats (distance, heatmaps, zone time) and extract 30-second highlight clips per side. 6. **Optimization & Handoff:** Profile the pipeline on Jetson, optimize using TensorRT, and document the codebase for your team’s integration. **Relevant Skills** ✔ **Core CV:** Object Detection (YOLO), Multi-Object Tracking (DeepSORT, ByteTrack), Pose Estimation (MediaPipe, OpenPose) ✔ **Computer Vision Tools:** OpenCV, Homography, Camera Calibration, GStreamer, RTSP ✔ **Edge AI Deployment:** NVIDIA Jetson, TensorRT, CUDA, Docker, Linux Shell Scripting ✔ **Backend & Data:** Python, FastAPI (for API endpoints), JSON Structuring, Data Pipelines ✔ **Analytics:** Heatmap Generation, Spatial Statistics, Time-Series Data Processing **Next Steps** I am confident in delivering a robust vision pipeline tailored for your Jetson hardware. If you’d like, I can prepare a lightweight 48-hour demo showing player detection and basic tracking on a sample video to validate the approach before we proceed.

@craigd43

Hi, This is exactly the kind of computer vision pipeline I enjoy building. I have experience developing AI and vision systems using Python and OpenCV, including object detection, multi-object tracking, camera calibration, and real-time video processing. My approach would be to build a robust detection → tracking → court-mapping pipeline, ensuring stable player identities across occlusions before generating reliable movement analytics, highlight events, and structured match outputs. I'm comfortable working on headless Linux environments over SSH, optimizing pipelines for NVIDIA Jetson devices, and delivering clean, well-documented code that integrates smoothly with your existing platform. I'd be happy to discuss the architecture and help turn your padel courts into truly smart courts.

@Web1devloper

As a seasoned professional in Data Analytics & Science with over 8 years of experience, my expertise is deeply aligned with your project requirements. With skills ranging from **Machine Learning (particularly Python), Data Visualization to Camera Calibration**, I am confident in my ability to create and deploy the AI vision models for your smart padel courts project. My prior work spans multiple industries including finance, healthcare, e-commerce and SaaS. This diverse range of exposure has equipped me with adaptable skills and unique problem-solving approaches that are transferrable to this project. To address the core challenge of player detection, tracking and maintaining stable per-player identity across occlusion and crossover, I propose applying **YOLO-class detection with ByteTrack/DeepSORT-style tracking techniques**. Additionally, I can leverage my proficiency in **OpenCV** to robustly map player movement into real court coordinates using homography and even explore auto-calibration using court lines. Moreover, my extensive background working on constrained hardware platforms such as NVIDIA Jetson, optimizing models for real-time video processing fits well with your deployment context. Being well-versed in the JetPack stack (CUDA, TensorRT).

@melopgabriel

Hi, This is a very interesting project, and I'd love to help build the computer vision pipeline for your smart padel courts. I have strong experience with Python, OpenCV, Linux, and real-time video processing. My approach would be to build a robust pipeline using YOLO for player detection, ByteTrack/DeepSORT for tracking, OpenCV homography for court mapping, and pose estimation for gesture-based highlight detection. The output would include player tracking data, movement analytics, heatmaps, highlight clips, and structured JSON for your frontend. I'm comfortable working remotely on headless Linux systems over SSH and optimizing applications for NVIDIA Jetson devices. I'd be happy to discuss your camera setup and technical requirements in more detail. Thanks, Gabriel

@PrateekP3003

Hello, I have experience building real-time computer vision pipelines in Python using OpenCV and modern deep learning models for object detection, multi-object tracking, pose estimation, and spatial analytics. I can develop an optimized detection → tracking → court-mapping pipeline using YOLO with ByteTrack/DeepSORT-style tracking to maintain stable player identities across occlusions, apply homography for accurate court-coordinate mapping, generate movement analytics such as heatmaps, distance, and zone-time, and trigger highlight clip generation through pose- or event-based logic. The entire pipeline will produce clean JSON outputs, clips, and analytics that integrate seamlessly with your existing player-facing platform. I'm also comfortable working in headless Linux environments over SSH, deploying and optimizing models for NVIDIA Jetson devices using OpenCV, CUDA/TensorRT where appropriate, and integrating RTSP/GStreamer streams for real-time inference. The solution will be modular, well-documented, and designed for future extensions such as rally segmentation and shot detection. Best regards, Prateek

@nexvative

Hi, Your smart padel court project is exactly the kind of computer vision challenge I enjoy. I have strong experience building real-time AI pipelines using Python, OpenCV, PyTorch, YOLO, DeepSORT/ByteTrack, pose estimation, and multi-camera tracking for production-grade applications. For this project, I would develop a robust pipeline that detects and tracks all four players with stable identities, maps movements to real court coordinates using homography, generates player analytics (heatmaps, distance covered, zone occupancy), and automatically extracts highlight clips based on configurable triggers such as gestures and player actions. The solution will be optimized for NVIDIA Jetson using TensorRT where appropriate to ensure reliable real-time performance. My approach includes: • YOLO + ByteTrack/DeepSORT for accurate player detection and tracking • OpenCV homography with court-line calibration • Pose estimation for gesture-based event detection • Clean JSON outputs, highlight clips, and analytics • Modular, well-documented Python code with easy future enhancements I'm comfortable working remotely on headless Linux systems via SSH, using RTSP/GStreamer pipelines, and optimizing models for embedded GPU environments. I'd be happy to discuss your current setup and propose the best architecture for a scalable, production-ready vision pipeline. Looking forward to collaborating with you.

@rbtech1984

Hi There!!! ★★★★ (AI vision pipeline for real-time player tracking, court mapping & Jetson optimization) ★★★★ I’ve carefully read your project and understand you need a computer vision specialist to build a robust pipeline for smart padel courts. From dual-camera feeds, the system must detect, track, map player positions, generate performance analytics, and create highlight clips while running efficiently on NVIDIA Jetson hardware. ⚜ Player detection & multi-object tracking ⚜ Pose estimation & gesture recognition ⚜ Court calibration & homography mapping ⚜ Movement analytics & heatmap generation ⚜ Jetson CUDA/TensorRT optimization ⚜ OpenCV, Python & RTSP/GStreamer integration ⚜ Clean documentation and maintainable code I have strong experience building AI-powered computer vision systems using Python, OpenCV, YOLO-based detection, tracking algorithms, and GPU optimization. I've worked on projects involving real-time video analytics, object tracking, and performance tuning for edge devices. My approach is to first validate the detection pipeline, then optimize identity tracking, implement accurate court mapping, and finally generate reliable analytics and highlight outputs with well-structured, easy to maintain code. I'd be happy to discuss your technical workflow, share relevent experience, and explain how I'd approach the full detection → tracking → mapping pipeline. Looking forward to hearing from you. Warm Regards, Farhin B.

@novalitztech

Hi, I see you're looking to turn padel courts into smart courts using AI vision models. The goal is to track players in real-time, create highlight clips, and provide performance statistics. To tackle this, I would first focus on setting up a robust detection and tracking system using YOLO for player identification, combined with DeepSORT to maintain player identities throughout the match. Next, I would work on pose estimation to trigger highlights and use OpenCV for court mapping to convert the camera feed into real court coordinates. My experience with similar projects allows me to efficiently process noisy tracking data into meaningful analytics like heatmaps and movement statistics. I’m comfortable with the NVIDIA Jetson platform and have done optimizations for constrained hardware before, ensuring smooth performance. I believe in building clean, scalable solutions that provide value to users. Best regards, Novalitz Tech

@joshuak485

Hi, this is Joshua from Davis. I have strong experience building Python vision systems with OpenCV and edge deployment. I understand you need a robust pipeline for two camera feeds, from detection to tracking to court mapping and match output. The goal is stable player identities, real court positions, and useful clips and stats. I can build a practical model stack that turns noisy match video into reliable per match results. I would use YOLO for player detection, ByteTrack or DeepSORT for identity tracking, and pose cues for gesture triggers. Then I would add homography based court mapping, smooth the 5 Hz tracks, and export JSON, clips, and heatmaps in a clean format. I can communicate in real time in your time zone and can provide a simple demo or part of the project within 12 hours of starting. Q1: What camera angles and resolutions are the two feeds using? Q2: Do you already have labeled video for players, court lines, or poses? Q3: Should highlight triggers rely only on pose, or also on rally and motion patterns? Best, Joshua

@iterativeTech

As an experienced AI and Cloud Data Engineering specialist, my approach aligns seamlessly with the objectives of transforming data into measurable business outcomes. I have a deep understanding of the core services you require, including modern object detection, multi-object tracking, pose estimation, and camera calibration all using Python and OpenCV - which I use extensively in my work. My prior experience encompasses building and optimizing scalable ETL/ELT pipelines and leveraging real-time analytics to deploy intelligent models. Moreover, I'm well-versed in optimizing models for constrained hardware like the NVIDIA Jetson that your system uses. My familiarity with the Jetson's JetPack stack is valuable in this context. I usually work on headless Linux boxes remotely over SSH, communicating via scp or running/debugging everything from the command line - all skills required for this position. My ability to speak your language; Cuda and TensorRT, my skills in deploying models on devices and transforming noisy real-world tracking data into robust metrics make me particularly suitable for this role. Implementing streaming data pipelines, interactive dashboards using PowerBI, and a host of other applications mentioned in your project are not new to me - rather they form part of my daily repertoire.