论文标题
基于CNN的实时基于CNN的分割体系结构，用于单个视图设置中的球检测
Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup
论文作者
论文摘要
本文考虑了在具有挑战性但常见的情况下从单个角度来检测球的任务，在这种情况下，球经常与球员相互作用，而在背景方面形成鲜明对比。我们通过将问题提出为有效的CNN体系结构解决的分割任务来提出一种新颖的方法。为了利用球动力学，该网络用一对连续的图像馈送。我们的推论模型可以实时运行，而不会通过时间分析引起的延迟。我们还表明，测试时间数据的增加允许大幅提高检测准确性。作为另一个贡献，我们将公开发布该工作所基于的数据集。
This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.
