Improving radial lens distortion correction with multi-task learning

Abstract

With computer vision and machine learning, sports image analysis can enhance viewer engagement and contribute to the overall understanding of sports events. However, beneath the apparent simplicity of sports images lies a complex challenge of radial distortion, which can impede their accurate interpretation. The need for high precision and real-time performance is paramount. We present a new regression-based method for radial distortion correction that improves the accuracy of distortion model coefficient prediction by introducing a secondary learning task that compares the distortion level of two random training samples. The secondary task requires no additional annotation, and because it is also related to radial distortion, it encourages the common feature extractor component to learn more general and robust features while preventing overfitting and improving the efficiency of training data. We have evaluated our proposed method using two public datasets and compared it against five other methods. Our method surpassed them all, both in the accuracy of the radial distortion correction and speed as well. You can find the source code and trained models at https://vgg.fiit.stuba.sk/improving-radial.