  In recent years, there has been increased interest in video summarization and
automatic sports highlights generation. In this work, we introduce a new
dataset, called SNOW, for umpire pose detection in the game of cricket. The
proposed dataset is evaluated as a preliminary aid for developing systems to
automatically generate cricket highlights. In cricket, the umpire has the
authority to make important decisions about events on the field. The umpire
signals important events using unique hand signals and gestures. We identify
four such events for classification namely SIX, NO BALL, OUT and WIDE based on
detecting the pose of the umpire from the frames of a cricket video.
Pre-trained convolutional neural networks such as Inception V3 and VGG19
net-works are selected as primary candidates for feature extraction. The
results are obtained using a linear SVM classifier. The highest classification
performance was achieved for the SVM trained on features extracted from the
VGG19 network. The preliminary results suggest that the proposed system is an
effective solution for the application of cricket highlights generation.
