Computer Vision Engineer - Tennis Video Detection Pipeline

@AwaisChaudhry

Hello, I understand you need a comprehensive computer-vision pipeline for analyzing tennis match footage. My approach involves creating a robust system that not only identifies players, balls, and rackets but also classifies their actions and shot types during rallies. Utilizing a combination of libraries like YOLO and OpenCV, I will ensure accurate detection and tracking, alongside generating valuable metrics for player performance. The system will ingest MP4 files and output structured data, including a human-readable analysis with improvement suggestions. This ensures players get helpful feedback to enhance their game. What specific performance metrics or player insights are you most interested in tracking? How many matches will you provide for training and evaluation? Do you have any preferences for the model architecture we should use? What are your expectations regarding the latency of the detection pipeline? Are there any specific formats required for the JSON output? Will you be able to provide labeled clips for training if needed? What is your timeline for the project milestones? Do you have any existing infrastructure for integrating the API or script? Thanks,

@LiveExperts

Hello, With over a decade of experience in the computer science and engineering fields, specifically in computer vision, I am confident in my ability to deliver an effective end-to-end solution for your tennis video detection pipeline project. My extensive expertise with TensorFlow, PyTorch, YOLOv5/8, Detectron2 and OpenCV are perfect matches for your stack preference. Additionally, I've worked on sports analytics projects using object detection, pose estimation and object tracking - skills that align directly with your requirements. One key aspect of our offer is our ability to adapt and fine-tune our models to maximize accuracy based on the data available. Our strong background in machine learning will enable us to build a detection and classification pipeline that not only recognizes players, rackets, balls, poses and shot types but also compute various timing and technical metrics needed for generating structured JSONs like the strengths and improvements for each shot. To provide a holistic view for post-match analysis, we'll include overlays for further insights. Moreover, in delivering this solution, I aim to make it useful across various platforms through API integration or script delivery – ensuring accessibility and effectiveness. To assure you of my commitment to meeting deadlines effectively while maintaining optimum quality work under budget, I will provide you with accurate benchmarks for both latency and cost per vide Thanks!

@SPARKtechBD

Since 2015 I have been working in C/C++/C# programming and 10(ten) years of experience in C/C++/C# programming. Windows Desktop Application, Console Application, Image Processing and have knowledge in Driver Development in C. Expert in data structure building and Object Oriented Programming (OOP). Have a great experience in C++ MFC and C++ WinUI 3 for GUI design and development. Also expert in C/C++ GPU CUDA programming. If you want a good delivery of the project, then send me a message, please.

@irsolutions

I am a seasoned Computer Vision Engineer with a strong track record in developing end-to-end pipelines for sports analytics, and I am excited about the opportunity to work on your Tennis Video Detection project. I understand the challenges you face in needing a robust system that can accurately detect players, balls, rackets, classify shot types, and provide insights for improvement based on shot quality. With my expertise in computer vision using tools like YOLO, TensorFlow, and OpenCV, I have successfully delivered similar projects in the sports domain, including pose estimation and object tracking. My experience aligns perfectly with your requirements, and I am confident in my ability to create a reliable and efficient detection and classification pipeline for your tennis footage. I have a proven track record in delivering high-quality results within budget and timeframe constraints. Let's discuss your project requirements further and align on milestones and dataset access to kick start this exciting project. I am ready to dive in and create a solution that exceeds your expectations.

@ShaikhAneesa

Hello, i’ve reviewed your project and fully understand the need for a complete tennis-analytics pipeline that detects players, rackets and balls, classifies shot types and provides clear performance insights. With strong experience in YOLO-based detection, pose estimation and sequence classification, I can build a fast, accurate and fully reproducible system tailored to your footage. I will start by implementing object detection using Ultralytics YOLOv8 or YOLO11, followed by pose extraction through MediaPipe or MoveNet. These outputs will be linked with Deep SORT or ByteTrack to maintain consistent player identities. Once tracking is stable, I will develop a PyTorch classifier that learns shot types from pose sequences and ball-trajectory patterns. Using these features, the system will compute strengths, improvement points and quality scores, and produce clean JSON along with optional GPT-4o feedback. The final solution will run as a clear Python pipeline or API for seamless integration. To ensure perfect alignment, could you share a short sample clip so I can assess movement quality and camera angles? • Ready to see why I'm the right fit for your project? Head over to my profile for client reviews and a glimpse of my portfolio: https://www.freelancer.com/u/ShaikhAneesa Best Regards, Aneesa.

@corpmember29

HELLO, WE HAVE WORKED ON SIMILAR PROJECTS AND CAN PROVIDE EXAMPLES. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE, WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ZERO TO PUBLISHING ON STORIES. Hello, I’ve reviewed your requirements for the end-to-end tennis computer-vision detection and classification pipeline, and I fully understand the scope — accurate player/ball/racket detection, pose estimation, shot-type classification, player tracking, and automated feedback with scoring from 0 to 100. I am having 10+ years of experience in computer vision, sports-analytics models, and pose-based action recognition using YOLO (v5–v11), Detectron2, OpenCV, DeepSORT/ByteTrack, and PyTorch. I can build a reproducible pipeline that ingests MP4, detects all entities, extracts pose sequences, classifies shot types, and outputs structured JSON plus GPT-based human-like feedback. My approach includes dataset preparation, model training, pose-sequence classifier, tracking consistency across rallies, metric computation, and delivery of an API/script ready for integration. Latency and cost benchmarks will also be included. I eagerly await your positive response. Thanks, Christina

@softsolution2000

I am confident that my skills in C Programming, Python, Software Architecture, C++ Programming, and OpenCV make me a great match for the Computer Vision Engineer - Tennis Video Detection Pipeline project. I am eager to discuss the full project scope and adjust the budget accordingly. My priority is to deliver high-quality results within your budget and timeline. Please review my 15-year-old profile to see my extensive experience and commitment to client satisfaction. I am ready to start working on this project and showcase my dedication. Looking forward to hearing from you.

@Specgi

As a seasoned computer-vision engineer, my expertise lies in building robust pipelines to solve intricate image recognition problems à la your tennis video detection and classification pipeline requirement. With proficiency in widespread AI frameworks like TensorFlow, PyTorch and a track record of employing YOLOv5/8, Detectron2, and Deep SORT for accurate object detection, I am indeed equipped to deliver measurable outcomes for your project. My experience isn't limited to general-purpose computer-vision either: I've extensively worked on sports-analytics and pose-estimation projects in the past. This means that besides offering solid detection abilities for players, balls, rackets, I can also tackle the challenge of classifying shot types by efficiently tracking player movement throughout a rally with reliable related metrics. I could even provide strengths and tips aimed at improving movements thus allowing a practical understanding of the shots' quality.

@jmvdesignsohio

Hi, I’m a graphic/UI designer experienced in creating crisp, scalable icon systems for modern web apps. I can redraw your existing map icons as clean SVGs that stay razor-sharp on 1×/2×/3× screens, aligned with your brand palette and UI tone. I’ve delivered similar icon refresh projects for SaaS dashboards and logistics apps, where clarity, accessibility tags, and consistent styling were critical. All icons will be rebuilt as vector assets, optimized with proper viewBox settings, logical naming, and accessibility-friendly <title>/<desc> tags. You’ll also receive 2× PNG fallbacks, organised in a tidy asset folder ready for immediate drop-in replacement. Best regards, Joseph

@KarthikTechCon

Hello, I’m Karthik, a Computer Vision & AI engineer with 10+ years of experience building end-to-end CV pipelines, pose-estimation systems, and sports-analytics models. I can deliver a complete tennis-video detection + action-classification pipeline with accurate tracking, shot recognition, scoring, and AI-generated coaching feedback. What I will build: ✔ Object detection (players, racket, ball) using YOLOv8/YOLO11 or Detectron2 ✔ Pose estimation via MediaPipe, MoveNet, or SensiAI ✔ Tracking using Deep SORT or ByteTrack for consistent player identity ✔ Shot-type classifier trained on your labelled data (forehand, backhand, smash, volley, bandeja, etc.) ✔ Movement + timing metrics (speed, reaction time, swing angle, ball trajectory) ✔ Technical scoring model (0–100) + strengths & improvement suggestions ✔ Structured JSON output, e.g.: { "type_of_shot": "bandeja", "strengths": [], "improvements": [], "score": 82, "overlay_url": "" } ✔ Human-style coaching feedback using GPT-4o or similar LLM ✔ Latency + cost benchmarking ✔ Deployment as a REST API, CLI script, or notebook for easy integration Why me: • Strong CV background (YOLO, pose estimation, OpenCV, PyTorch, TensorFlow) • Built sports-analytics pipelines (badminton, cricket tracking, fitness pose correction) • Clear milestone planning + reproducible results Ready to start once we finalise dataset sharing & milestones.

@juanjosec63

Hello, I’m excited about the opportunity to build your end-to-end tennis analytics pipeline. I have solid experience with computer vision in Python using YOLO (Ultralytics), OpenCV, and tracking frameworks like Deep SORT/ByteTrack, as well as pose-estimation setups that feed into sequence classifiers. I can design a reproducible pipeline that ingests MP4, detects players / rackets / ball, tracks each player over time, classifies shot types, computes technical and timing metrics, and outputs structured JSON exactly in the format you described, including GPT-4o–style feedback for strengths, improvements, and a 0–100 score per shot. I’ll also benchmark latency and cost per video and wrap everything in a clean API or script so it’s straightforward to integrate into your existing tools. Once we align on milestones and dataset access, I can start right away and iterate with you on model performance and coaching-quality feedback. Best regards, Juan

@annes01

This sounds like something I’d genuinely enjoy building. Your need for an end-to-end computer vision pipeline that automatically detects and classifies players, balls, and rackets aligns perfectly with my expertise in developing streamlined, intuitive solutions for sports analytics. I have extensive experience with YOLO and pose estimation, allowing me to deliver practical, measurable results that enhance performance analysis. Happy to outline how I would turn this plan into a working solution. Chat soon, Anne S

@devbaloni1983

Looking forward to working with you! Hello, Hope you are doing well! I am a PHP developer with strong experience in building secure, scalable, and high- performance web applications. I focus on delivering clean code, responsive design, and seamless functionality using modern PHP frameworks and best practices. What I Deliver: 1. High-quality PHP applications tailored to business needs 2. Secure user authentication and role-based systems 3. API development and third-party integration 4. Fast, optimized and responsive websites 5. Complete documentation and ongoing support Why Choose Me: 1. Clean, maintainable and scalable code 2. On-time delivery 3. Strong communication and problem-solving skills 4. Experience with both small and large-scale web projects Let's Get Started Share your requirements or a sample/reference website — I will provide: 1. Best approach 2. Timeline 3. Cost estimate

@pavlisim

I worked on projects that involved key point estimation: one use case was image clustering, another one hand pose estimation and the third one being facial key point recognition and 3D mesh creation in order to perform a swap. I like tennis as well, so I would love to talk more about your project!

@jovani81

With extensive computer vision experience, including a tennis analytics project using YOLOv8 and MediaPipe, I’ve built real-time detection and pose-tracking systems that faced challenges like fast-moving ball detection and consistent player identification across rallies. By integrating ByteTrack for multi-object tracking and fine-tuning YOLO on domain-specific data, I improved ball detection recall by 42% and maintained player ID accuracy above 94% across occlusions. I’ll deliver a complete pipeline: from MP4 ingestion through detection (players, racket, ball), pose estimation, shot classification, and JSON output with strengths, improvements, and shot score (0-100). The system will use YOLOv8 for detection, MediaPipe for pose, a transformer-based classifier for shot type, and GPT-4o for feedback generation—all containerized and API-ready. I’ll ensure low latency (<150ms per frame) and provide full benchmarking. Let's start with a discovery sprint to assess your footage and define evaluation metrics. I’m ready to begin immediately.

@oleksandrf3

Empowering Your Tennis Analytics with Precision and Insights. The main technical requirement for your project is to architect an end-to-end Python-based computer vision pipeline leveraging state-of-the-art object detection (YOLOv8/11), pose estimation, tracking, shot classification, and GPT-4o-powered feedback generation, all packaged for reproducibility and API/script integration. With a strong background in sports analytics, computer vision, pose estimation, and multiple projects for ball sports like tennis and soccer—featuring robust object detection, tracking, and movement analysis—I am well-versed in combining YOLO, MediaPipe, OpenCV, and sequence classifiers to deliver detailed metrics and actionable insights. My approach will ensure accurate player, racket, and ball detection, consistent identity tracking, action recognition, and quantitative plus qualitative feedback as structured JSON, ready to integrate. I look forward to your response. Thank you.

@volodymyrkoval

Hi Carlos, I'm excited about the opportunity to develop the end-to-end computer vision pipeline for tennis video detection. With over 10 years of experience in computer vision and deep learning, I specialize in object detection using frameworks like YOLO and OpenCV, making me well-equipped to tackle this project. I will ensure accurate detection of players, balls, and rackets while also providing insightful analytics on performance through structured JSON output. My approach will involve using your provided match footage to create a robust model that tracks player movements and evaluates their actions effectively. I am ready to start immediately upon aligning on milestones and dataset access. Best regards,

@ahmaddar2970

Hello there, I’ve read your tennis-video pipeline brief and I’m confident I can deliver an accurate, end-to-end system that detects players, rackets, balls, and poses, tracks players consistently through rallies, classifies shot types, computes timing/technical metrics and produces human-like feedback and structured JSON outputs. I have hands-on experience building YOLOv8/Detectron2 detectors, ByteTrack/DeepSORT tracking and pose-based sequence classifiers (LSTM/Transformer). My approach: Roboflow/YOLOv8 for detection, MediaPipe/MoveNet for pose, ByteTrack for consistent IDs, a shot-classifier trained on temporal features, a scoring heuristic to produce strengths/improvements, GPT-4o for feedback and an API for integration. I will benchmark latency and per-video cost and provide reproducible code and Dockerized deployment. Initial timeline: prototype (data & baseline) in 7–10 days, full pipeline + benchmarks in ~21 days. Best regards, Muhammad Ahmad

@johncalro

Hi, hope you are doing well. I've read your proposal very carefully, and I am confident about your project. I understand that you need an end-to-end computer vision pipeline to analyze tennis match footage, which involves detecting players, balls, and rackets, while classifying their actions and providing performance metrics. I have hands-on experience in computer vision, particularly with YOLO and pose estimation, and have successfully developed similar solutions in the past. My approach will include: - Utilizing YOLOv8 for accurate object detection and tracking of players, rackets, and balls. - Implementing a robust classification system to differentiate shot types and compute technical metrics. - Generating structured JSON outputs and human-like feedback using GPT-4 to enhance user experience. I can start immediately and complete the work within a short timeline. Looking forward to your reply.