  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}.

Computer Vision and Pattern Recognition