  Annually the gaming industry spends approximately $15 billion in marketing
reinvestment. However, this amount is spent without any consideration for the
skill and luck of the player. For a casino, an unskilled player could fetch ~4
times more revenue than a skilled player. This paper describes a video
recognition system that is based on an extension of the Mask R-CNN model. Our
system digitizes the game of blackjack by detecting cards and player bets in
real-time and processes decisions they took in order to create accurate player
personas. Our proposed supervised learning approach consists of a specialized
three-stage pipeline that takes images from two viewpoints of the casino table
and does instance segmentation to generate masks on proposed regions of
interest. These predicted masks along with derivative features are used to
classify image attributes that are passed onto the next stage to assimilate the
gameplay understanding. Our end-to-end model yields an accuracy of ~95% for the
main bet detection and ~97% for card detection in a controlled environment
trained using transfer learning approach with 900 training examples. Our
approach is generalizable and scalable and shows promising results in varied
gaming scenarios and test data. Such granular level gathered data, helped in
understanding player's deviation from optimum strategy and thereby separate the
skill of the player from the luck of the game. Our system also assesses the
likelihood of card counting by correlating the player's betting pattern to the
deck's scaled count. Such a system lets casinos flag fraudulent activity and
calculate expected personalized profitability for each player and tailor their
marketing reinvestment decisions.
