Ball (soccer, tennis, cricket etc.) tracking on a live streaming using Machine Learning algorithm Object detection and tracking is an essential component of many computer vision applications. One such application is ball tracking in live streaming using machine learning algorithms like YOLOv5. To implement this, the first step is to collect a large dataset of images or videos showing the ball in different positions and angles. These images or videos are then labeled using a tool like Makesense, where a bounding box is drawn around the ball in each image. The annotations are then exported in YOLO format, and the YOLOv5 model is trained using a deep learning framework like PyTorch or TensorFlow. Once the model is trained, it can be tested on new images to see how well it can detect and track the ball. Finally, the model can be integrated into a live streaming system using video processing libraries like OpenCV or FFmpeg. Overall, the process requires a solid understanding of computer vision, machine learning, and deep learning, as well as experience with programming in Python and using popular libraries. image->makesense-website->colab->trainmodel->input-video->output-detection