4 accepted papers
The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition…
Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Predicting the labels of future unobserved activities where no frames of the predicted activities have been observed is a challenging problem,…
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of `meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with…
Activity recognition in video has recently benefited from the use of the context e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled and entirely available at the outset. In contrast, we formulate a continuous learning framework for contex…