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RICAPS: residual inception and cascaded capsule network for broadcast sports video classification

The field of broadcast sports video analysis requires attention from the research community. Identifying the semantic actions within a broadcast sports video aids better video analysis and highlight generation. One of the key challenges posed to sports video analysis is the availability of relevant datasets. In this paper, we introduce a new dataset SP-2 related to broadcast sports video (available at https://github.com/abdkhanstd/Sports2). SP-2 is a large dataset with several annotations such as sports category (class), playfield scenario, and game action. Along with the introduction of this dataset, we focus on accurately classifying the broadcast sports video category and propose a simple yet elegant method for the classification of broadcast sports video. Broadcast sports video classification plays an important role in sports video analysis as different sports games follow a different set of rules and situations. Our method exploits and explores the true potential of capsule network with dynamic routing, which was introduced recently. First, we extract features using a residual convolutional neural network and build temporal feature sequences. Further, a cascaded capsule network is trained using the extracted feature sequence. Residual inception cascaded capsule network (RICAPS) significantly improves the performance of broadcast sports video classification as deeper features are captured by the cascaded capsule network. We conduct extensive experiments on SP-2 dataset and compare the results with previously proposed methods, and the results show that RICAPS outperforms the previously proposed methods.