AI-Based Football Video Analytics – Research Project

@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

@mattafareed4848

Hello, I’m interested in developing your AI-based football video analytics research prototype. I have experience with Python, YOLO, OpenCV, player tracking, performance metrics, experimental evaluation, visualizations, and academic research. I can deliver the complete prototype, genuine experimental results, and a well-structured research paper with proper references. Regards\ Muhammad

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with AI-Based Football Video Analytics – Research Project I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statisticaal programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, AutoCAD, 2D drawing, 2D draftingPCB, Electronics, Arduino, Embedded Systems Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@richard031596

You need a research-grade football analytics prototype that produces real detection, tracking, and player-performance measurements from your existing footage, with reproducible experiments rather than fabricated results. I can build the Python/OpenCV pipeline around YOLO/PyTorch for player detection, ByteTrack or BoT-SORT for tracking, then extract trajectories, distance, estimated speed, intensity, and zone coverage. I can generate detection/tracking metrics, FPS and processing time, player-level tables, graphs, and annotated video outputs. The methodology and results can then be documented clearly for the research paper. I’ve worked on CNN-based image processing and Python AI/ML pipelines, including computer-vision workflows where quantitative outputs matter. I’d keep pretrained models where appropriate and focus the budget on a reproducible experimental prototype. I’d estimate 3 days for a focused prototype and initial results. Can you share the video resolution, duration, and whether any ground-truth annotations are available?

@tyronet2

You need a research prototype that turns football footage into measurable player detection, tracking, movement, and performance metrics, backed by genuine experiments rather than fabricated results. I have 9+ years of Python and AI/ML development experience, including CNN-based image processing, real-time data pipelines, and analytical visualization. I can structure the pipeline around YOLO with ByteTrack or BoT-SORT, OpenCV and PyTorch, then generate player-wise trajectories, distance, speed estimates, zone coverage, intensity metrics, annotated videos, and quantitative evaluation results. I’ll keep the methodology reproducible and document the experimental setup, limitations, comparisons, tables, and visualizations clearly. For the paper, I can organize the technical findings into the requested research structure with appropriate references based on the actual experiments.

@Visharaa2024

I can build the research prototype using Python, PyTorch, OpenCV and ByteTrack/BoT-SORT, with genuine evaluation metrics for detection, tracking and player performance. I’ll deliver the working pipeline, player-wise analytics, annotated videos, graphs/tables, architecture diagrams and a complete research paper with literature review, methodology, experiments, results and references. No fabricated results. Questions: What is the video resolution/FPS and total footage duration? Do you have player ground-truth annotations? Should I select the best YOLO and tracking combination based on experiments? Do you already have a target Scopus/IEEE/Springer venue? Best Regards VDinfotech

@InjectAI

I read the requirements carefully. This project closely matches my background in Computer Vision, Deep Learning, YOLO, object tracking, Python, and academic research. I can develop the research prototype while keeping every reported result reproducible and based on actual experiments. My approach would be: • Preprocess and sample the football footage • Use YOLO/PyTorch for player detection and evaluate Precision, Recall, F1 and mAP • Implement ByteTrack or BoT-SORT for persistent player IDs • Calculate IDF1, MOTA, ID switches and tracking performance where ground truth permits • Estimate player trajectories, distance, speed, movement intensity and zone coverage • Generate player-wise performance profiles from measurable video evidence • Benchmark FPS and processing time • Produce annotated videos, plots, tables and comparative experiments • Clearly document assumptions and limitations, especially for speed/distance estimation without calibrated camera data I have 5+ years of Computer Vision and Deep Learning application experience, including object detection, tracking, satellite imagery, drone detection and deployment-oriented CV work. I can also structure the research paper around your required sections, including literature review, research gap, methodology, experimental results, discussion, limitations and references. I will not fabricate metrics or experimental findings.

@pooja10verma

With over a decade of experience in full-stack development and specializing in crafting robust front-end interfaces, efficient back-end systems, and intuitive mobile apps, I am more than equipped to tackle your AI-Based Football Video Analytics project. I have honed my skills in Python, OpenCV, YOLO/PyTorch to ensure that the research-level prototype we build will be exactly what you envision to assist in grassroots football talent identification. gements. My goal is to consistently deliver high-quality products; which I guarantee with my clean, maintainable coding style. I understand the intricate nature of your project and am skilled in all areas necessary for it's successful execution. My work involves extensive use of Python, OpenCV, YOLO/PyTorch that aligns perfectly with your requirements. Moreover, my ability for research work stems not only from understanding code but also from contributing relevant content. I would effectively construct a compelling sports analytics research paper as per the IEEE-standard format you outlined. My track record of working on academic projects and publishing papers will definitely cater to your goal of targeting legitimate Scopus-indexed venues. Together we can create something remarkable for the football industry!

@asthaj52

Hi, I have experience with Python, OpenCV, YOLO, and Machine Learning, and I can develop this research-oriented football video analytics prototype. My approach will include: * YOLO-based player detection * ByteTrack/BoT-SORT player tracking * Movement, trajectory, speed & zone analysis * Quantitative evaluation (Precision, Recall, F1, mAP, IDF1, FPS) * Graphs, annotated videos, and a complete research paper with proper references I will provide clean, well-documented source code and genuine experimental results from your football footage. I’m ready to discuss the dataset and experimental setup before starting. Regards, Astha Jain

@Algorista92

Hi, nice to greet you. This is Matías speaking from Córdoba, Argentina. I am the CEO and founder of MJE Data Consulting, specialized in data science, machine learning, computer vision, statistical analysis, and AI solutions. I can help you develop the research prototype using Python and a reproducible computer vision pipeline covering video preprocessing, YOLO player detection, multi-object tracking, movement analysis, feature extraction, and player-level performance metrics. I understand this is a research prototype, not a commercial application, so I would focus on genuine, reproducible results, including detection/tracking metrics, FPS, processing time, player analysis, trajectories, movement visualizations, annotated videos, and comparisons where feasible. I can also structure the experimental results into the proposed research paper, including methodology, experiments, quantitative results, comparative analysis, discussion, limitations, conclusions, tables, graphs, workflow architecture, and properly cited references. I would like to review the football footage, camera perspective, research objectives, and computational environment to define the most appropriate approach and experimental scope. Best regards, Matías

@ilkerozer88

I know sklearn, pytorch, pandas, numpy, seaborn, matplotlib. I can complete the project in 7 days. I am asking for INR 2,350 from you.

@fadwam12

Hello, I’m interested in working on your AI-based football video analytics research project. I have experience with Python, Machine Learning, and data analysis, and I’m interested in applying these skills to Computer Vision and sports analytics. I understand the proposed workflow, including video preprocessing, YOLO-based player detection, object tracking using ByteTrack or BoT-SORT, feature extraction, movement analysis, and generating quantitative player performance metrics. I can work on the research prototype in Python using OpenCV, PyTorch, and suitable pretrained models. I can also help with the experimental evaluation, including detection and tracking metrics, processing performance, player-wise analysis, graphs, tables, and annotated video outputs. I understand the importance of using genuine experimental results rather than fabricated results, and I will make sure that the reported results are based on the actual experiments performed on the provided footage. I can also organize the methodology, experiments, results, limitations, and references into a clear research-paper structure. I would be happy to discuss the available football footage, expected outputs, and experimental setup before starting. Best regards, Fadwa

@mosar3

Hi, I can build your football video analytics prototype using Python, YOLO/PyTorch, OpenCV and ByteTrack/BoT-SORT. I’ll handle player detection, tracking, movement/trajectory analysis, performance metrics, quantitative evaluation, visualizations and reproducible experiments, with results based only on actual data. I can also work with you through the research methodology and paper preparation. Ready to start by reviewing your footage.

@joeyb05

Not happy with the final result? Don’t pay. Your satisfaction comes first. I recently helped a client turn their idea into a clean, professional, and user-friendly solution, and I’d love to do the same for you. With extensive experience in AI/ML, Computer Vision, and sports analytics, I can develop your experimental research prototype efficiently. I specialize in player detection using YOLO, tracking with ByteTrack/BoT-SORT, and generating performance metrics that provide genuine quantitative results. My focus on quality, usability, and detail ensures that every aspect of your project, including the research paper, meets academic standards. I am committed to making adjustments until you’re completely satisfied. Let’s discuss your project further and ensure we create a prototype that not only meets but exceeds your expectations. Regards, Joey

@ganeshrakate

Hi, With 4+ years in applied ML/DL and 7+ years in academia, I combine computer vision engineering with formal scientific methodology to deliver both your research prototype and a publication-ready paper. Approach & Execution: • Tracking Pipeline: Python, OpenCV, and PyTorch deploying pretrained YOLO for player detection paired with ByteTrack/BoT-SORT for persistent multi-object tracking. • Metric Extraction: Perspective transformation to map bounding boxes to 2D pitch coordinates, extracting quantitative metrics: distance, estimated speed, intensity, and zonal heatmaps. • Empirical Rigor: Genuine evaluation with no fabrication, calculating detection (Precision, Recall, mAP), tracking (IDF1, MOTA/ID switches), and FPS benchmarks. Deliverables: Clean, reproducible source code with annotated output videos Publication-ready manuscript structured for Scopus-indexed venues (IEEE/Springer format), including literature review, architecture diagrams, results tables, and academic citations Individual analytical player profiles and comparative charts My academic writing experience ensures proper paper structure, rigorous methodology, and compliant formatting. I am committed to timely delivery and authentic research outcomes within your budget. Let’s connect to discuss your footage and kick off the project. Best regards, Ganesh Rakate

@yuvraj231925

I am a BCA Graduate i completed the certification course of Data Science i am having skills of python, sql, excel, tableau, power bi, machine learning and artificial intelligence

@altafa24

Hi, I’d love to work on your project. I have hands-on experience with Python, DSA, Pandas, NumPy, Machine Learning, and YOLO algorithms. I also work with SQL and MongoDB for data handling and databases. Along with development, I have experience in AI-based projects and video editing as well. I focus on clean, practical and reliable work, and I’m ready to start right away. I’d be happy to discuss your requirements and build the solution according to your needs.

@hamdyh0

Hello, I’m interested in this project. I have experience in Python, Computer Vision, YOLO, OpenCV, PyTorch, and Machine Learning, including a YOLO-based video analysis project with object detection and distance estimation. I can implement the detection → tracking → movement analysis → performance metrics pipeline using YOLO with ByteTrack/BoT-SORT, and provide genuine experimental results, graphs, annotated videos, and the required research paper. I understand the importance of reproducible results and will not fabricate any metrics. I’m ready to review the provided football footage and start with the experimental setup. Best regards, Hamdy Hamada

@CliveMSoftware

I have direct experience building YOLO-based detection and ByteTrack/BoT-SORT tracking pipelines for sports video analysis. I'll develop your grassroots football talent ID prototype following your exact workflow: video preprocessing, player detection with precision/recall metrics, multi-object tracking with IDF1 and MOTA evaluation, and feature extraction for performance profiles. I'll generate genuine experimental results, create comparative analysis tables, produce annotated video outputs with trajectories and zones, and write a publication-ready research paper with proper literature review, methodology diagrams, and results visualization suitable for IEEE or Springer venues. All deliverables include well-documented Python source code, architecture diagrams, and rigorous quantitative validation.