AI Frame Detection Engine

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Sports Detection Engine for Video Frames Analysis ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a sports detection engine. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in sports data analysis. I will build the engine to process video frames, identify sports, logos, players, and match info effectively. ➡️ Why Me? I have 5 years of experience in developing APIs and video processing solutions. My skills include Python, FastAPI, C#, and data handling. I also have a strong grip on technologies like machine learning and image processing, ensuring your project will be efficient and reliable. ➡️ Let's have a quick chat to discuss your project in detail, and I can show you samples of my previous work. I'm excited to collaborate and deliver exactly what you need. ➡️ Skills & Experience: ✅ Python Programming ✅ FastAPI Development ✅ ASP.NET Core ✅ Video Processing ✅ Data Analysis ✅ Machine Learning ✅ API Integration ✅ Debugging ✅ Image Recognition ✅ Database Management ✅ Performance Optimization ✅ Secure Coding Practices Waiting for your response! Best Regards, Zohaib

@gunslinger

Hi, I read through your requirements and this is more about building a reliable vision pipeline than just running models on frames. You’re combining sport classification, logo detection, player identification, and scoreboard extraction in a continuous stream, so the key is structuring it as a multi stage pipeline with separate models and a shared state per frame batch. I’d handle this in Python with FastAPI, using workers to process frames in parallel while keeping outputs consistent and ordered, and design it to run both with and without GPU so it stays stable in different environments. Not a generic bid, I went through the details and this is something I can build cleanly and make production ready. Kindly contact me for further discussion.

@HNMN3

As an acclaimed software company that has been in operation for over a decade, STR Softwares LLP has always prided itself on being at the forefront of cutting-edge technologies, such as Deep Learning and Machine Learning (ML), both of which are central to the successful implementation of your AI Frame Detection Engine. Our elite Python development team has a proven track record of delivering premium and reliable solutions, two traits your project holds in high regard. We have built and deployed several projects involving object detection and data classification similar to what you've described. Our commitment goes beyond mere project completion. We prioritize client satisfaction through technical excellence, constantly pushing the boundaries to deliver quality code in a timely manner. We understand and appreciate the nature of your project's IP. You can rest assured that confidentiality and security will be rigorously maintained throughout our partnership. STR Software's experience in cloud solutions dovetails perfectly with your project's requirement for scalability and flexibility across different environments. Whether it's on Windows, Linux or with or without GPU - our solutions will smoothly transition across these systems and give you seamless service. Overall, from our seasoned specialists down to our communication approach, STR Softwares LLP is best positioned to help you actualize this incredible undertak

@OlektraGroup

Hi, I understand you need a robust AI frame detection engine to accurately identify and extract specific events or frames from video streams. Whether you are dealing with high-motion sequences or subtle visual changes, the key is implementing an efficient temporal analysis pipeline that balances inference speed with detection precision. I have built several computer vision solutions involving YOLO-based object detection, including license plate recognition and deepfake detection, and have extensive experience converting models like YOLOR to TFLite for deployment. For this project, I would likely leverage a lightweight backbone with frame-differencing logic or a temporal CNN to minimize latency while maintaining high recall. I have previously optimized similar Python-based detection systems for production environments. Are you planning to run this inference on edge hardware, or will it be processed via a cloud-based GPU server?

@Feriver

Hello, I can develop the AI Frame Detection Engine using Python with FastAPI for cross-platform Windows support, leveraging deep learning models for sports classification, logo detection, player identification, and home/away determination. The engine will process incoming video frames in real-time, store results in memory, and return structured data to the frontend via the API. Multiple worker processes (starting with 3) will handle concurrent streams, scalable as needed. Match info extraction from overlays will also be implemented using OCR. The engine will support both GPU and CPU environments, with a secure, bug-free architecture suitable for integration into your larger system. Training will follow the provided folder structure for logos, wears, and ball images, ensuring accurate recognition. Full source code, documentation, and deployment instructions will be provided, with IP ownership fully transferred to Sors Markets Ltd. Clarification Questions: Are player images pre-labeled with names/numbers, or should the engine extract numbers and match to a separate roster file? For logo and match-info detection, do you require real-time frame-by-frame processing, or is near real-time acceptable for high resolution videos? Thanks, Asif

@devendrathakur12

i’ve done very similar recently with multi-model video pipelines (yolo + clip + ocr) running via fastapi workers and streaming frame inference will this run on live stream fps or batch frame ingestion from recorded video? how many concurrent frames per second per worker do you expect initially? i suggest separating detection into modular pipelines (sport, logo, player, ocr) so each can scale independently and not block latency. i also suggest caching embeddings for logos and jerseys to speed repeated matches and improve consistency across frames. i will first set up a fastapi service with worker pool and gpu/cpu fallback, then integrate yolo/ocr/embedding models with your folder-based training. next i will add stateful memory per worker and optimize batching and response latency. Best, Dev S.

@quan9824

Hi, I have solid experience building computer-vision backends for real-time image/video analysis and can develop this as a secure Windows-compatible engine with FastAPI (or ASP.NET Core if preferred). My approach would use a modular worker architecture so multiple workers can be initiated/reused independently, process continuous frame streams, and return structured results in memory with low latency. The engine will cover: • sport classification • logo detection • player number/name recognition from shirts • home/away kit classification • scoreboard extraction from the top-left overlay I would design it to run in both GPU and CPU Windows environments, with scalable worker management beyond the initial 3 workers. Training will follow your folder structure, with separate pipelines for sport detection, logo/team wear classification, OCR/player recognition, and scoreboard parsing. Delivery includes full source code, setup instructions, and a secure, testable API ready for integration into your larger platform. I understand reliability and security are critical, and I build with validation, fault isolation, and clean documentation so the system can be stress-tested on your side. I’m happy to share relevant CV/AI experience and discuss how I’d structure the demo and deployment.

@juanjosec63

Hi, I’m available to start right away. I have strong experience in Python, FastAPI, C#, computer vision, deep learning, multi-worker inference pipelines, and building secure backend APIs for real-time image and video analysis on Windows environments. I’ve worked hands-on with detection and recognition systems like this, and I can build a worker-based engine that processes continuous video frames, identifies sport type, logos, players, kits, and scoreboard data, then returns structured results reliably in GPU and non-GPU setups with clean source code ready for integration into your larger platform. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@maximg10

⭐Hello, I'm ready to assist you right away!⭐ I believe I'd be a great fit for your project since I have extensive experience in Python, FastAPI, Neural Networks, Machine Learning, Computer Vision, and API Development. My expertise aligns perfectly with the requirements of your AI Frame Detection Engine project. I have successfully implemented similar solutions in the past, ensuring high accuracy and efficiency in frame analysis. My track record includes developing secure and reliable systems, which aligns with your project's strict security criteria. With my skills and experience, I am confident in delivering a robust and scalable frame detection engine that meets your specifications. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@kamran2012

Good to see this project, I will build the AI frame detection engine — sport classification, logo recognition, player identification via shirt numbers, and home/away kit detection — served through a FastAPI backend with configurable worker pools. For logo and kit detection, I will use a fine-tuned YOLOv8 model trained on your folder structure, with OCR post-processing for scoreboard extraction and jersey text. This approach runs well on both GPU and CPU environments. Questions: 1) What frame rate will the workers receive — full video FPS or sampled intervals? 2) Do you have a minimum dataset size per team/logo for initial training? Looking forward to potentially working together. Thanks, Kamran

@a4logic

Hello, I understand the wide scope and niche requirements of this project, and I can confidently say that my expertise in C# programming aligns perfectly with your needs. I've been leading a team at Our Software, where we've implemented various complex web services, including several AI-based solutions, and garnered excellent feedback on our deliverables. We always emphasize on incorporating the latest technologies to ensure optimal results, which is evident in your preference for FastAPI (Python) or an ASP.NET Core API (C#), both choices we are competent with. Front and back-end integration is also part of our core competency. We can securely store and process large amounts of data efficiently, as well as build intuitive and user-friendly systems. In line with your project's need for scalability, you'll be delighted to know we never shy away from projects that need flexibility or those that demand growth. Security is a top priority for us - so much so that we intentionally challenge our code ourselves to ensure there are no vulnerabilities or loopholes. Lastly, as an established web service provider, we fully respect intellectual property rights. You can be assured that the source code will solely belong to Sors Markets Ltd. Our focus has always been maximizing client satisfaction and it reflects in the long-term relationships we maintain with our esteemed clients. Given a chance, I am confident that our collaboration can help tur Thanks!

@FaizalShaik80

Hello, I can deliver what you need. I’ve carefully reviewed your requirements, and this is essentially the same type of project I completed two months ago. I am a skilled freelancer with 6+ years of experience in Python, C# Programming, FastAPI, Machine Learning (ML) and I can deliver the results as quickly as possible. Please visit my profile to check the latest work and honest client reviews. Connect in chat to discuss details and next steps. Talk soon.

@hotanloc

Hello, I have already completed a similar project successfully. Project review: https://www.freelancer.com/projects/python/people-detection-counting/reviews Please take a look at my previous Freelancer projects and reviews. Your project aligns well with my experience, and I will do my best to meet all your requirements. My core expertise is in object detection, tracking, and counting. I have developed many computer vision projects, including: * People detection and counting * Product detection and counting * Defect detection in manufacturing * Vehicle detection and speed analysis I have strong experience in image processing and CCTV video analysis, where objects are detected and counted from images or video streams. For your project, I will: 1. Train a model using annotated data to generate optimized weights 2. Develop the detection system based on the trained model 3. Analyze detected objects and display the results clearly My technical stack includes YOLO, OpenCV, TensorFlow, PyTorch, Keras, OCR, and other ML/DL frameworks. With my experience in machine learning and deep learning, I can build an accurate detection system and implement post-processing (such as object counting and analysis) using OpenCV. I am confident I can deliver a high-quality solution within a short timeframe. Please feel free to send me a message so we can discuss your project in more detail. I look forward to hearing from you. Thank you.

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience with a background in computer vision and real-time detection systems. I am familiar with Python, FastAPI, PyTorch, OpenCV, deep learning. For this project, the most important part is building a fast and reliable multi-stage detection pipeline, because sport type, logos, players, kits, and scoreboard data all need different recognition logic but must return together in real time. I will build a worker-based engine for Windows that supports GPU and non-GPU environments, processes frames continuously, and returns structured results through a clean API with scalable worker management. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@arbu1499

Hi Ken, I’ve worked on computer vision pipelines that process video frames in real time, and your requirement for multi-signal detection (sport type, logos, players, kits, scoreboard) with a scalable worker-based API is very clear. I can build this using a modular FastAPI backend with GPU/CPU compatibility, integrating models for object detection (sports/logos), OCR (scoreboard + jersey numbers), and classification (home/away kits), all orchestrated through reusable workers. I’ll design the system so each worker processes frames independently, stores structured results in memory (or Redis if scaling), and streams responses efficiently to the frontend. The training pipeline will align with your folder structure, allowing easy extension for new teams, players, and sports. The final solution will be Windows-compatible, secure, well-tested, and delivered with clean, documented source code for integration into your larger system. Looking forward for your positive response in the chatbox. Best Regards, Arbaz Ali

@ianb65

Hello, there! I have experience building computer vision pipelines for sports detection and real-time frame processing, including logo recognition, OCR, and player identification. My approach: • Use a modular CV pipeline (YOLO/Detectron + OCR) to detect sport type, logos, players, kits, and scoreboard data • Implement a FastAPI backend with scalable worker architecture (multi-worker, queue-based) • Support both GPU and CPU environments on Windows • Ensure real-time processing with in-memory caching and clean API responses • Design training pipeline based on your folder structure for easy extension The system will be stable, secure, and ready for integration into your larger platform. I can deliver clean, well-documented source code and complete testing on your environment. Best regards, Ian Brown

@techsty2014

Hello, I will build your frame detection engine with a Windows-ready API, reusable worker architecture, and continuous frame processing for sport type, logos, player shirt identity, home or away kit, and scoreboard extraction. The system will support GPU and non-GPU environments, return structured results to the front end, and include source code ready for integration into your larger platform. I would split detection into separate pipelines for scene classification, logo and kit recognition, OCR, and player ID mapping so workers stay faster, easier to scale, and more secure under continuous load. Questions: Will you provide labeled training data for players, logos, kits, and score overlays from day one? Do you prefer FastAPI in Python or ASP.NET Core in C# for the production API? Should worker memory persist only during runtime, or also write results to a local database or cache? Send me a message and we can go over the details. Best regards, Faizan

@Techroj

Dear Client, Greetings!! I am a Python and AI/ML developer with 7 years of experience in Machine Learning, Deep Learning, and Computer Vision. I have worked on projects involving object detection, image classification, and video frame analysis, including real time sports and event recognition systems.I understand your project requirements fully.I am experienced in building FastAPI based solutions that handle multiple workers, scale efficiently, and run on both GPU and non GPU environments on Windows/Linux. I can also implement secure, bug free, and production ready code that integrates seamlessly with your existing system.I have worked on similar computer vision and sports analytics projects, training models for logo recognition, jersey number detection, and player identification, and I can design a modular system where multiple workers process frames in parallel while maintaining memory efficiency. I am confident that I can deliver a robust engine ready for integration, along with full source code and documentation. Looking forward to collaborating and delivering a high quality solution. Regards, Rojan.U

@Jairo51019

https://www.freelancer.com/projects/software-development/Parser-for-SQL-Migrations/reviews Hi, I am confident in my ability to create an AI Frame Detection Engine that meets your strict criteria. Throughout my career, I have developed numerous dynamic and scalable web applications which demanded precision and robustness, much like your project does. Demonstrating my proficiency in leveraging technologies such as Python and ASP.NET Core API (C#), I can incorporate the desired features into your engine while ensuring compatibility with both GPU- and non-GPU Windows/Linux environments. I'm well-versed in delivering secure, reliable and bug-free code, making me the ideal candidate to handle your diverse set of requirements. My experience with similar tasks empowers me to comprehend and interpret patterns from frames efficiently - from identifying sports types to detecting recognizable logos, home/away players' inference to tracking scores on screens. Let's schedule an interview to discuss how we can move forward together and possibly turn this task into additional opportunities for us. With me as part of your team, expect nothing short of a reliable and comprehensive solution delivered on time. Thank you for considering my candidacy!