Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Activity Modeling Using Event Probability Sequences
IEEE Transactions on Image Processing
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We present a novel approach to mining dependency rules that explain the scenes present during a video sequence. The approach first characterizes activities based on their most important events. Next, an HMM-based approach finds the mixture components that best describe the clustering dependencies between events and activities in video data. The dependencies among activities are taken as association patterns with temporal precedence and analyzed using their co-occurrence relationships in time windows. This technique is meant to understand the multiple actions taken in a video or to predict future occurrences of certain activities.