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Convolutional Recurrent architectures are currently preferred for spatio-temporal learning tasks in videos to the 3D convolutional networks which accompany a huge computational burden and it is imperative to understand the working of different architectural configurations. But most of the current works on visual learning, especially for video anomaly detection, predominantly employ ConvLSTM networks and focus less on other possible variants of Convolutional Recurrent configurations for temporal learning which warrants a need to study the different possible variants to make informed, optimal design choices according to the nature of the application at hand. We explore a variety of Convolutional Recurrent architectures and the influence of hyper-parameters on their performance for the task of anomaly detection. Through this work, we also intend to quantify the efficiency of the architectures based on the trade-off between their performance and computational complexity. With comprehensive quantitative and visual evidence, we establish that the ConvGRU based configurations are the most effective and perform better than the popular ConvLSTM configurations on video anomaly detection tasks, in contrast to what is seen from the literature.

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Game Theory concepts have been successfully applied in a wide variety of domains over the past decade. Sports and games are one of the popular areas of game theory application owing to its merits and benefits in solving complex scenarios. With recent advancements in technology, the technical and analytical assistance available to players before the match, during game-play and after the match in the form of post-match analysis for any kind of sport has improved to a great extent. In this paper, we propose three novel approaches towards the development of a tool that can assist the players by providing detailed analysis of optimal decisions so that the player is well prepared with the most appropriate strategy which would produce a favourable result for a given opponent's strategy. We also describe how the system changes when we consider real-time game-play wherein the history of the opponent's strategies in the current rally is also taken into consideration while suggesting.