Football Auto-Tracking Pole Camera

@HamAbbasi00

As an experienced Full Stack Developer specializing in OpenCV, I bring a unique blend of programming skills and a passion for creating cutting-edge solutions that align perfectly with your project needs. Over the years, I have honed my abilities to build high-quality, efficient applications, leveraging on image processing technologies like the one you propose. My comprehensive understanding of databases will also allow me to seamlessly integrate your streaming and recording pipeline requirements. To demonstrate my capabilities, I would develop a fully-functional prototype using the hardware you've specified. Adhering strictly to your specifications, I would utilize OpenCV's robust image-recognition algorithms to effectively track the ball and drive the servos accordingly. Moreover, my strength in developing detailed technical documentation will ensure the transfer of necessary knowledge for setting up and reproducing results - a crucial factor in determining the success of such projects. Rest assured, I am dedicated to delivering not just a product that meets your expectations but also long-term customer satisfaction. Let's discuss further on how I can make your vision a reality!

@jobink15

This project delivers a fully autonomous AI-powered football tracking camera mounted on a fixed pole beside a 6-a-side turf. The system uses real-time computer vision (YOLOv8 or YOLOv11 + TensorRT) to detect and track the ball without GPS or external sensors. A Jetson Xavier NX/Orin Nano handles inference while a high-resolution Sony IMX477 or similar camera provides 1080p–4K footage. Ball coordinates are translated into smooth motion using PID-controlled pan-tilt servos, ensuring low latency and stable framing. A configuration interface allows tuning sensitivity, colour thresholds, ball size, and servo behaviour based on lighting or turf type. Video is processed through GStreamer to enable simultaneous live streaming (RTMP/HLS to YouTube or local displays) and high-quality local recording. The system runs fully hands-free once started—automatically locking onto gameplay and following rapid direction changes. Power is stabilized through a regulated 12V–5V supply to protect the servos and compute unit. A setup guide and wiring schematic ensure the system can be replicated and mounted on any standard outdoor pole. Performance will be validated through a 10-minute real-play test where the ball must remain centred with minimal frame drops.

@yesminee3

I will deliver a complete real-time football auto-tracking camera system using advanced computer vision and deep learning. The solution will detect and track the ball using YOLO/TensorRT or an optimized model, and drive smooth pan-tilt servos with low latency. I will provide full hardware specifications, wiring diagrams, streaming/recording setup, and configurable software for sensitivity and speed control. The final system will follow the ball accurately during fast 6-a-side play, stream live to your platform of choice, and record high-quality 1080p video, fully reproducible with clear setup instructions.

@aryas071

I have been working on Object detection and image recognition projects from quite a long. I hv been using yoloV8 to train my models but with time I have found the better training models. I have made gesture recognition and object following robots using this. I have presented my challenge paper on one shot object detection in CVPR paris. Hence I hv got a lots of experience in real time object detection using open cv and integrating yolo infrastructure in it.