Moving Ball Detection with Coarse-to-Fine Image Cropping
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Solution Overview
Problem
Conventional methods for detecting a moving ball in a series of images, such as frame difference techniques, incur high computational costs due to pixel-by-pixel comparisons, and suffer from increased false positives when reducing computation time.
Innovation Solution
A two-step machine learning process involving a first neural network for coarse ball detection followed by a second neural network for fine ball detection, where images are resized and cropped to reduce computational expense and false positives, using synthetic training data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame difference techniques are used to detect moving balls by comparing pixel values between image frames, then ball detection can be performed, but computational cost increases significantly due to pixel-by-pixel comparisons
Solution Approach 1:
The patent extracts only the necessary information (ball presence and location) from the entire image by using object detection models that process images and output bounding boxes around detected objects, rather than processing every pixel. This extraction approach reduces computational cost while maintaining detection capability.
Solution Approach 2:
The patent uses pre-trained object detection models (such as YOLO or Faster R-CNN) that have been trained on large datasets to recognize balls. These models create a simplified representation of the image data through learned features, allowing fast inference without pixel-by-pixel comparison, thus reducing computational power requirements.
2Loss of time
If images are resized to smaller dimensions to reduce computation time for ball classification, then computational time decreases, but false positives increase
Solution Approach 1:
The patent segments the detection process into multiple stages: first using a fast object detection model to identify potential ball locations in the original-resolution image, then applying a classification model only to the cropped regions around detected objects. This segmentation allows the system to maintain high accuracy by processing only relevant regions at full resolution while using computational efficiency for the initial screening.
Solution Approach 2:
The patent applies different processing qualities to different parts of the image. The entire image is processed at full resolution for object detection, but only the local regions around detected objects are processed in detail for classification. This local quality approach maintains detection accuracy for potential balls while reducing overall computational time by avoiding detailed processing of the entire image.
Data Source
AI summary
In a first step, a time series of images are resized to a smaller dimension, and the smaller images are fed into a first classifier that is trained to classify as a ball any objects in the smaller images that resemble a ball. In a second step, the smaller images are mapped back to the series of images, and regions in the series of images that contain the mapped ball are cropped from the series of images. The mapped ball is shifted based on a velocity of the mapped ball in the cropped regions, and the second classifier regresses center coordinates and a radius of the shifted ball, classifies whether the shifted ball is the ball based on a confidence score, and updates the shifted ball in the cropped regions based on the regressed center coordinates and radius.