Detection of activities and events without explicit categorization

We address the problem of unsupervised detection of events (e.g., changes or meaningful states of human activities) without any similarity test against specific models or probability density estimation (e.g., specific category learning). Rather than estimating probability densities, very difficult to calculate in general settings, we formulate the event detection as binary classification with density ratio estimation [9] in a hierarchical probabilistic framework. The proposed method takes pairs of video stream data (i.e., past and current) as input with differing time-scales, generates density ratio models in a way of online learning, and judges if there is any `meaningful difference' between them based on the multiple density ratio estimations. Through experimental studies on real-world scenes of specific domains using challenging datasets from sports scene (i.e., tennis match) with complex background, we demonstrate the potential advantage of our approach over the state-of-the-art in terms of precision and efficiency.