Toward a causal topic model for video scene analysis

Unsupervised of different types of activity in video data has many applications such as anomaly detection, automated tagging of video for search, and cognitive modeling. Topic models originally used in corpus analysis have recently been used to identify different types of activities in videos. Among topic models, probabilistic latent semantic analysis (pLSA) provides an efficient method for identifying clusters of activity in video. This paper integrates pLSA with the causal graphical models of Pearl [1] to learn visual event structures and their temporal relationships simultaneously. The model is fully generative. A noisy-OR style temporal dependence is used for learning which is well known to identify the same causal patterns that human learners do. The addition of temporal learning allows the system to model temporally ordered and long range temporal dependencies that traditional topic models cannot. The model successfully identifies human recognizable event structures in video and successfully classifies videos of human activity learning.