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Vision based player detection is important in sports applications. Accuracy,efficiency, and low memory consumption are desirable for real-time tasks suchas intelligent broadcasting and automatic event classification. In this paper,we present a cascaded convolutional neural network (CNN) that satisfies allthree 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. Weconducted experiments on basketball and soccer games. Experimental resultsdemonstrate that our method can accurately detect players under challengingconditions such as varying illumination, highly dynamic camera movements andmotion blur. Comparing with conventional CNNs, our approach achievesstate-of-the-art accuracy on both games with 1000x fewer parameters (i.e., itis light}.

Light Cascaded Convolutional Neural Networks for Accurate Player Detection