论文标题
曲棍球广播视频中的玩家身份
Player Identification in Hockey Broadcast Videos
论文作者
论文摘要
我们提出了一种深刻的卷积神经网络（CNN）方法，以解决NHL广播视频中的曲棍球玩家识别问题。玩家识别是一个困难的计算机视觉问题，主要是因为玩家的外观，遮挡，面部和物理特征模糊。但是，我们可以通过处理播放器的可变长度图像序列（又称“ tracklets”）来观察玩家的球衣数字。我们提出了一个可端到端的可训练Resnet+LSTM网络，具有残留网络（RESNET）基础和长期的短期内存（LSTM）层，以发现球衣数字随时间的时空特征并学习长期依赖性。对于这项工作，我们创建了一个新的曲棍球播放器Tracklet数据集，其中包含曲棍球播放器边界框的序列。此外，我们采用二维卷积神经网络分类器作为晚期得分级融合方法来对Resnet+LSTM网络的输出进行分类。在我们的新数据集的测试拆分中，这实现了总体播放器识别精度得分超过87％。
We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spatio-temporal features of jersey numbers over time and learn long-term dependencies. For this work, we created a new hockey player tracklet dataset that contains sequences of hockey player bounding boxes. Additionally, we employ a secondary 1-dimensional convolutional neural network classifier as a late score-level fusion method to classify the output of the ResNet+LSTM network. This achieves an overall player identification accuracy score over 87% on the test split of our new dataset.
