Vision based player detection is important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasting and automatic event classification. In this paper, we present a cascaded convolutional neural network (CNN) that satisfies all three of these requirements. Our method first trains a binary (player/non-player) classification network from labeled image patches. Then, our method efficiently applies the network to a whole image in testing. We conducted experiments on basketball and soccer games. Experimental results demonstrate that our method can accurately detect players under challenging conditions such as varying illumination, highly dynamic camera movements and motion blur. Comparing with conventional CNNs, our approach achieves state-of-the-art accuracy on both games with 1000x fewer parameters (i.e., it is light}.

本文介绍了一种满足精度、效率和低内存消耗的级联卷积神经网络 (CNN)，用于实现基于视觉的运动员检测。通过从标记的图像块训练一个二进制分类网络，然后在测试时高效地将该网络应用于整个图像，本方法在篮球和足球比赛中得到了精确的运动员检测结果，并在聚光灯影响、高度动态的相机移动和运动模糊等多种复杂条件下表现出色。与传统CNN相比，本文方法参数少1000倍，准确度更高。

基于光瀑式级联卷积神经网络的准确球员检测