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.
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