Football & Fitness Video Analytics Pipeline

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@Web1devloper

As an experienced developer with over 8 years of work in both app and web solutions, and adept in the Python programming language; I'm confident that I've got the skills necessary to handle your Football & Fitness Video Analytics Pipeline project. To ensure rock-solid fitness and performance metrics, I'll use robust computer-vision tools like YOLO/Detectron for detection, DeepSORT/StrongSORT for tracking and standard football-model calibration for distance mapping. I'll build a repeatable pipeline that imports raw CCTV videos, detects and re-identifies each player accurately, tracks their movements throughout the match, and then exports a detailed CSV/JSON report along with simplified visual overlays. I'm committed to delivering outputs that meet or exceed your set acceptance criteria such as player identification accuracy at ≥90 % over the full match, distance error ≤10 % when compared to a manual benchmark, goal/shot detection precision at ≥80 % recall ≥75 %. To ensure transparency always, I will provide a comprehensive README file that explains not only model choices but also demonstrates how to run the code locally (preferably in Python) and how to retrain if there are any camera angle or lighting changes. Choosing me for this project isn't just selecting someone who can get the job done technically, but also someone who can meticulously document and containerize the codebase in order to make it scalable. Let's embark on this exciting journey together!

@rohank2502

Your CCTV already filmed the matches. I will turn every recording into per-player minutes, distance, calories, shots and goals. I can start right now. In 24-48 hours you get a live sample on YOUR footage: players followed, a first CSV, and a simple overlay to watch. The hard part is keeping the right identity through a full uncut match and tagging shots and goals to the right person. I will prove that on two of your matches first. Reports in CSV/JSON, overlays, and a short guide if the camera or lighting changes. Can you share two full uncut sample matches and whether players wear numbered bibs?

@mikes126

I will build an end-to-end Python computer-vision pipeline using YOLO + StrongSORT/ByteTrack, pitch calibration and football-specific event logic to convert your fixed CCTV matches into reliable per-player fitness and football metrics. The main challenges are maintaining player identity through occlusions, accurately mapping image movement to real pitch distance, and distinguishing shots/goals from normal ball movement. I will address these with tracking/re-identification, homography-based pitch calibration, temporal smoothing and event validation. Pipeline: (1) ingest and preprocess CCTV, (2) detect players/ball, (3) track and re-identify players across the full match, (4) calibrate pitch coordinates, (5) calculate minutes, distance and speed profiles, (6) estimate calories using player/activity parameters, (7) detect and timestamp shots/goals, (8) link events to players, and (9) export CSV/JSON plus optional MP4 overlays. I will validate the system against your two full sample matches and tune thresholds/models to target ≥90% player identification, ≤10% distance error, and ≥80% precision / ≥75% recall for goals/shots. I recommend providing pitch dimensions, camera resolution/FPS, team sheets and any known player/ground-truth data. This will significantly improve calibration and identity accuracy.

@naeem1114

The biggest hidden snag is the occasional overlap of players wearing similar kits, which can confuse the re‑identification step. I'll fine‑tune the appearance embeddings with a short‑term gallery per half so the tracker can keep each athlete distinct even during dense clusters. From those tracks I'll derive minutes, distance, calories and flag any shot or goal events for the final CSV. A common pitfall is relying on a single calibration frame; slight lens distortion can push distance error beyond ten percent. I'll add an automatic homography update using the corner markers in every half, keeping the mapping accurate throughout the match. Ready to start immediately and deliver the CSV/JSON reports plus an overlay MP4.

@NEHABHAT92

As a seasoned developer with a strong background in Python, I couldn't be more excited about the prospect of working on your Football & Fitness Video Analytics Pipeline. Over the course of my 9+ year career, I've honed my skills in computer-vision, machine learning, and data analysis -- all of which will be invaluable in turning your CCTV footage into clear, per-player metrics. Fueled by my passion for providing top-notch solutions, I'm confident in delivering a repeatable pipeline that can detect, re-identify, track and report player movements efficiently and accurately. Understanding the importance of fitness and football performance equally, I can devise an approach that prioritizes both aspects while adhering to your requirements. My familiarity with prominent frameworks like YOLO/Detrectron and DeepSORT/StrongSORT will surely come into play here. Rest assured, data quality is of utmost importance to me and I can guarantee solid numbers for minutes played, distance covered, calories burned as well as goals and shots. Lastly, my dedication to maintaining clean, documented, and well-performing code stands as a testament to the "README" you seek from me. As I get started on your project, know that if any additional metadata is needed- be it team sheets or pitch dimensions- I will intimate you beforehand to ensure comprehensive execution. Let me turn your match clips into actionable insights that can fuel improvement! Benefits? Count on effective cost p

@gowrik16

Hi, I can help you convert your PostgreSQL operational data into efficient, repeatable SQL reports for monitoring cycle time, throughput, and other workflow-efficiency metrics. My approach will be: 1. Schema review – Analyze the schema diagram and sample records to understand tables, keys, timestamps, workflow stages, and relationships. 2. Metric definition – Identify the relevant operational events and establish clear, reproducible calculations for cycle time, throughput, processing duration, and other efficiency indicators. 3. PostgreSQL development – Write structured and well-commented SQL using CTEs, aggregations, window functions, and appropriate joins where required. 4. Validation – Test the queries against the supplied sample output and verify edge cases such as NULL values, duplicate records, incomplete workflows, and timestamp issues. 5. Performance – Review expensive joins and filtering logic and optimize queries where appropriate while keeping the code readable and maintainable. 6. BI-ready output – Structure the results so they can be scheduled or directly consumed by a future dashboard/BI workflow. 7. Documentation – Provide .sql scripts and a concise README explaining how to run each query, what each metric represents, and where future modifications can be made. I can start by reviewing your schema and sample rows, then build and validate the reports incrementally so each metric is traceable and reproducible. Regards, K. Gowri

@rakhshanz3

With nearly 20 years of experience in the field of Artificial Intelligence and Machine Learning, I am confident that I can deliver exactly what you're looking for in your Football & Fitness Video Analytics Pipeline project. My expertise in computer vision and image processing, combined with my proficiency in Python - a pivotal component of your preference for the stack - will enable me to build a robust and efficient solution to handle the masses of CCTV footage you have. My proven track record in delivering accurate ML-based analytics platforms will ensure that the player identification, tracking, and event detection systems are precisely calibrated to meet your targets. I have led technical development for AI-driven projects involving large datasets like yours, guaranteeing that your desired per-player metrics: minutes played, distance covered, calories burned, goals, and shots - will be rock-solid. Furthermore, my skills in building Full Stack SaaS applications with modern dashboards will help me develop an intuitive interface that compiles the outputs into easily interpretible CSV/JSON reports accompanied by simple visual overlays. I also understand the importance of clean code and thorough documentation for the sustainability of any project. You can trust me to make this an integral part of my work output. Let me tackle this challenge for you with the combined expertise of academia and industry!

@abhilashr53

You want raw CCTV footage transformed into per-player fitness and football metrics, fully automated, with strict accuracy targets. Here is exactly how I build it. Pipeline: YOLOv8 for player and ball detection, BoT-SORT for multi-object tracking, and a jersey-colour plus body-silhouette re-ID layer to maintain player identity across occlusions and camera cuts. Homography projection maps pixel movement onto real pitch dimensions for distance and speed, feeding a MET-based calorie model. Goal and shot detection combines ball trajectory analysis with goal-region event classification, timestamped and linked to the nearest tracked player. Deliverables are a Python CLI pipeline, CSV and JSON reports per match, optional annotated MP4 overlays, and a README covering model choices, local setup, and fine-tuning steps if your camera angle or lighting shifts. My background is in computer vision pipelines built on PyTorch and Ultralytics, with real experience hitting production-grade accuracy targets on sports footage. Timeline: two weeks from receiving your sample videos, with a midpoint check-in after the first match clears acceptance criteria. One question before I scope the final hours: are your CCTV feeds a fixed single overhead angle across all five pitches, or do angles vary between venues?

@KinshukAgarwal

I measured a detect-then-recognise pipeline on 602 real low-res images before bidding. With a correct crop the recogniser scored 80%; a hand-tuned OpenCV detector found the target 30% of the time. A learned detector took localisation to 97.7%, end to end 29% to 82%. The finding that matters: your 90% player ID target lives or dies on tracking and re-identification. Distance and speed come from clean tracks and one homography to pitch metres. YOLOv8, ByteTrack, per-player CSV, MP4 overlay, containerised Python. Send one match and I return its first 10 minutes with tracks overlaid and ID switches counted, before you award. First match in 7 days. Do the teams wear distinct bib colours?

@Lenard007

I’ve been building a portfolio of modern web applications and digital platforms, and your project immediately caught my attention because it fits well with the type of work I’m focused on. I can help turn your idea into a clean, professional and user-friendly solution built around what you actually need. I noticed you mentioned the need for player identification accuracy of at least 90%. That’s something I’d pay particular attention to while keeping the overall experience smooth and reliable. My skills include computer vision, machine learning, and Python development. I have experience with frameworks like YOLO and DeepSORT, which are perfect for your requirements. You can view examples of my work at www.lenard007portfolio.com. I’d be happy to discuss your idea before anything is decided. Send me a message and we can see if I’m the right fit for your project. Regards, Lenard007

@shaun4014

Hello, I understand you’re seeking a robust computer-vision pipeline to analyze football and fitness metrics from your CCTV footage. I have extensive experience in developing similar systems and can ensure you receive precise, actionable insights. PROFESSIONAL QUALITY | FAST COMMUNICATION | UNLIMITED REVISIONS WHAT I CAN OFFER: - A repeatable pipeline to process raw video, featuring player detection and tracking throughout the match. - Accurate calculations for fitness metrics such as minutes played, distance covered, and calories burned. - Event detection for goals and shots, ensuring high precision and recall. - Clear CSV/JSON reports and optional visual overlays for comprehensive data presentation. - Detailed documentation, including setup instructions and retraining guidelines. MY APPROACH: I prioritize understanding your specific needs to deliver a solution that meets your targets. I am ready to start immediately and can adapt the tech stack as needed to ensure we hit accuracy benchmarks. Regards, Shaun Kelly

@brunogfc

Hi — I build production computer-vision pipelines and this is right in my lane. Plan: 1) Ingest raw CCTV, stabilize and undistort the frames. 2) Player detection + multi-object tracking (YOLOv8 + ByteTrack) with re-identification via appearance embeddings, so each player's ID survives occlusions on 5-a-side/7-a-side turf. 3) Pitch homography to convert pixel motion into real meters -> distance covered, speed zones, minutes played, and a calorie estimate from distance + intensity. 4) Ball, shot and goal detection for the football-performance metrics. 5) Clean CSV/JSON export per player + an annotated overlay video. To de-risk it: send me 1-2 minutes of your footage and I'll return a tracked clip + a metrics sample first, so you can check accuracy before committing to the full build. The final pipeline is repeatable and documented, so you can re-run it on any new match yourself.

@eternitech

Hi, New on Freelancer — 20 years of development experience behind us. We're taking our first few projects here at a fraction of our normal rate purely to build our review history. You get senior agency work at junior pricing; we get a review. Straight trade. implementing a YOLO-based model for player tracking can help identify and extract per-player metrics from your footage efficiently. I’d start by setting up a pipeline that processes each video frame to detect and analyze player movements. Can you provide access to a sample video to assess the footage quality?

@sownthariyaj

Hi — I'll be direct about your acceptance criteria, because they are not equally achievable and I'd rather tell you that now than at delivery. Distance ≤10% error is very achievable. Homography from the pitch markings maps image coordinates to real-world metres, and 5-a-side pitches have clean, known dimensions. One detail decides whether you hit 10% or miss badly: raw per-frame foot positions jitter, and naively summing frame-to-frame displacement inflates distance substantially — sometimes by 30% or more on a full match. It needs trajectory smoothing before integration. This is the single most common reason distance figures come out wrong, and it's a solved problem. Minutes played is straightforward once tracking is stable. Calories cannot be rock-solid, and no one bidding on this can make them so. There is no calorie signal in video. Any figure is a MET-based model applied to speed, distance and an assumed body mass — so accuracy depends on inputs you'd have to supply per player, and the model itself carries maybe 15-20% uncertainty even in sports science literature. I'll implement a defensible model and document exactly what it assumes. I won't pretend it's measured. Player identification ≥90% over a full match is where I'd want to test before committing. Appearance-based re-identification works by distinguishing players visually. On 5-a-side where one team wears identical kit, filmed from a fixed overhead angle, there is very little to distinguish them by — and every occlusion or player crossing is a chance for two identities to swap. The honest answer is that achievable accuracy depends on your specific footage: camera height, resolution, how often players bunch. It also depends on definition — 90% of frames, 90% of tracks, or fewer than N identity switches per match are three very different bars. Goals and shots at 80% precision is the hardest item. It requires reliable ball detection, and on overhead CCTV the ball is small, fast, motion-blurred and frequently occluded. Whether 80% is reachable is an empirical question about your footage, not something to assert in a proposal. So here is what I propose instead of promising four numbers sight unseen. Phase 1, measurement. Send me 10-15 minutes of representative footage. I build detection, tracking and homography, and produce a report giving the actual achieved figures for each of your four criteria against a manual benchmark on that clip, plus what would need to change to improve each one. You get a real answer about feasibility, and code you keep either way. Phase 2, full build, scoped and priced once we both know what the footage supports. Containerised, documented, README covering model choices and retraining as you asked. If Phase 1 shows 90% re-ID isn't reachable on this camera angle, you'll know for a few hundred dollars instead of finding out at the end of a five-figure project. If it is reachable, we proceed with the targets grounded in measurement. Things worth collecting now, since you offered: team sheets, exact pitch dimensions, camera height and mounting position, and video resolution and frame rate. Frame rate matters more than people expect — below 25fps, fast movement between frames degrades both tracking and distance. My bid is the floor of your range and covers Phase 2 on the assumption the footage cooperates. Phase 1 I'd price separately and small. Happy to be one of the bidders who says the targets need testing rather than one who agrees to all four upfront.

@nabeeli18

Greetings I can develop a robust computer-vision pipeline to analyze your football matches and deliver precise player metrics. I understand you're looking for a system that balances fitness and performance analytics, providing reliable data on minutes played, distance covered, goals, and shots. My approach will ensure accurate player identification and movement tracking while generating comprehensive reports in the required formats. I bring experience in building video analytics solutions tailored for sports, having successfully completed similar projects that meet stringent accuracy and performance criteria. I would love to chat about your project further and suggest a follow-up discussion to explore your specific needs and how I can help. Regards, Nabeel Ismail

@karthikresonite

Dear Client, Resonite Technologies is excited to propose our services for your Football & Fitness Video Analytics Pipeline project. Our team has extensive experience in computer vision and has successfully delivered similar projects, ensuring precision and reliability in video analytics. We will develop a robust pipeline that processes your CCTV footage to extract player metrics effectively. Our solution will include: - Player Tracking: Implementing state-of-the-art detection and re-identification algorithms, ensuring ≥90% accuracy throughout the match. - Fitness Metrics: Automatic calculations for minutes played, distance covered, speed profiles, and calorie estimates per player, with a distance error of ≤10%. - Performance Metrics: Detecting goals and shots with a precision of ≥80% and recall of ≥75%, accurately linking events to players. - Output Formats: Generating CSV/JSON reports and optional MP4 overlays, along with comprehensive documentation for local execution and retraining. We are committed to meeting your requirements and ensuring a clean, documented, and containerized codebase. Best regards, Karthik B Resonite Tech

@nikkibraiam

Hi, I'm a Software Architect with 11+ years building production computer-vision pipelines (Python, YOLO/Detectron detection, DeepSORT/StrongSORT tracking). I understand the real challenge here: it's not just detecting players, it's keeping stable per-player identity across a full uncut match from one fixed CCTV angle, then deriving accurate fitness (distance, minutes, calories) and football (goals, shots) metrics from that same tracking data without drift. What I'll deliver: ✓ End-to-end pipeline: detection → re-identification → tracking across the full match ✓ Fitness layer: distance, minutes on pitch, speed profile, calorie estimate per player ✓ Football layer: goal/shot event detection linked to player and timestamp ✓ CSV/JSON report + optional overlay MP4 ✓ README covering model choices, local run instructions, and retraining for camera/lighting changes I've built this type of system before. I work fast (10 days), I don't ask questions mid-project, and I deliver exactly what you need—nothing less. Let's build this right.