Mobile-based Sports Video Analysis Developer

@Mahihassi

With my extensive background in electrical engineering, firmware development, and PCB design; I am expertly positioned to deliver the cutting-edge software solutions your sports video analysis project demands. Having mastered languages like C, C++, and Python as well as tools such as OpenCV and YOLO, I bring a wealth of practical experience to the table. Throughout my career, I have worked on high-performance systems- FPGA systems with My-designs. With this, I can optimize your project for real-world conditions such as lighting, motion, and occlusion. Attributing my deep familiarity with video processing and frame analysis your mobile-based sports video analysis will fall well within my area of expertise. I thrive in building prototypes quickly yet practically, an approach that aligns perfectly with your project's needs. Furthermore, I have a strong track record in delivering AI/ML/Deep Learning integrated projects for edge devices-invoking the kind high-level analytics you're seeking. In conclusion, my commitment to delivering comprehensive and quality solutions positions me optimally for your project.

@MashoodurRehman1

I am a seasoned computer vision developer with extensive experience in building video analysis systems, specifically tailored for mobile platforms. With a strong proficiency in Python, OpenCV, and object detection frameworks like YOLO, I can effectively contribute to developing your mobile-based sports video analysis prototype. My background includes developing object detection models for applications requiring fast-moving object tracking and event detection. I understand the importance of syncing audio signals to enhance analysis accuracy, a skill I have applied in multi-sensory video projects. I've also worked with real-world video processing challenges, such as varying lighting and motion, which aligns with your project requirements. I am keen to leverage my expertise in sports video analysis and integrate with mobile systems. I'm interested in discussing how my skills can best be utilized to shape your prototype's technical approach. Could you provide more details on the types of sports and specific events you're targeting for detection?

@augurstech

A Warm Hello! We are readily available to start working on this project! We are confident to provide you real-world computer vision prototype for sports video analysis—especially involving fast-moving objects and mobile-captured footage—is both technically challenging and highly impactful. We’re excited about the opportunity to collaborate on this. We will provide you a proof-of-concept system that can: - Detect and track fast-moving objects (e.g., balls) - Analyse smartphone video input (1–2 camera angles) - Identify key events (impacts, motion changes, direction shifts) - Optionally leverage audio signals for event detection - Output structured data (timestamps, classifications) - Perform reliably under real-world conditions (lighting, motion blur, occlusion) We understand this is not just model development—it’s about building a robust, testable pipeline. We’re confident in delivering a functional prototype that demonstrates the core value of your concept and sets the foundation for future development. Best regards, Ana

@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.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re building an early-stage sports video analysis prototype that processes smartphone footage to track fast-moving objects, detect key events, and return structured outputs like timestamps and classifications. The focus seems to be getting a practical CV pipeline working quickly under real-world conditions like motion blur, lighting changes, and occlusion. I’ve worked on computer vision pipelines involving object detection, tracking, and video event extraction, so I understand the tradeoffs between accuracy, speed, and prototype scope. I would approach this by first validating the capture setup and target events, then building a lightweight detection/tracking pipeline in Python, testing it on sample footage, and structuring the outputs so the prototype can evolve into a more production-ready system later. I have a few questions to get a better understanding: Q1 – What sport is this for, and what exact object/events do you need detected in the first prototype? Q2 – Will you provide labeled sample videos already, or should the initial phase include dataset setup and annotation strategy? Q3 – Do you want the prototype to run offline as a backend processing pipeline first, or do you need near real-time performance from the start? Looking forward to hearing from you.

@irsolutions

With over a decade of experience in computer vision and high-scale systems, I understand your need for a skilled computer vision developer to assist in building a prototype system for sports video analysis on mobile devices. My background in scaling projects for over 1 million users and working in high-security FinTech aligns perfectly with the complexity of this endeavor. To ensure scalability and efficiency, a key strategy I recommend is leveraging a combination of Python with OpenCV for robust object detection and tracking. In a past project, I successfully built and scaled a similar system that processed video input from multiple sources, showcasing my capability to handle such tasks. I encourage you to reach out so we can discuss your project's roadmap further. I am eager to bring my expertise in computer vision and mobile development to the table to help you achieve your goals efficiently and effectively.

@OlektraGroup

Hi You need fast-moving object tracking, event detection, and structured outputs from mobile sports videos under real-world noise like motion blur and occlusion. This is exactly the kind of pipeline where detection + temporal reasoning must be tightly integrated, not treated separately. I’ve already built a tennis analysis system where I detect the ball, track its trajectory, and identify impact frames using motion spikes + trajectory discontinuity. For your case, I’d combine YOLOv8/RT-DETR for detection with DeepSORT/ByteTrack and add a lightweight temporal module (optical flow + velocity change heuristics) for precise event timestamps. I’ve also deployed similar pipelines on Jetson/mobile-grade systems, so performance optimization won’t be an issue. Do you already have labeled data, or should I design an annotation + auto-labeling pipeline as well?

@tangramua

Hello! My name is Olga - nice to meet you! I’m based in London, so I am sure that it will be convenient for you to cooperate with me as PM (Project Manager) and my team of developers. We’re excited about your sports video analysis prototype and would love to contribute our computer vision expertise to shape the right technical approach from the start. Our team has strong experience with Python, OpenCV, and YOLO-based detection pipelines, building systems that reliably track fast-moving objects under real-world conditions such as motion blur, lighting changes, and partial occlusion - exactly the challenges seen in mobile-captured sports footage. We’ve worked on motion tracking, event detection, and structured video outputs before, including frame-by-frame analysis, object trajectory tracking, and timestamped event classification. We’re also comfortable working with multi-angle inputs, synchronizing data streams, and optimizing pipelines for performance on lightweight or mobile-friendly systems. Familiarity with tools like Roboflow and dataset preparation allows us to move quickly from concept to a working prototype. Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews. Please contact us via Freelancer Chat to discuss your project in details. Best regards, Olga K Sales Department Tangram Canada Inc.