Light Cascaded Convolutional Neural Networks for Accurate Player Detection
This addresses the need for efficient and accurate real-time player detection in sports applications like broadcasting and event classification, representing a strong incremental improvement.
The paper tackles player detection in sports videos by proposing a cascaded convolutional neural network that achieves state-of-the-art accuracy on basketball and soccer games while using 1000x fewer parameters than conventional CNNs.
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}.