5 research outputs found
Action Recognition by Hierarchical Mid-level Action Elements
Realistic videos of human actions exhibit rich spatiotemporal structures at
multiple levels of granularity: an action can always be decomposed into
multiple finer-grained elements in both space and time. To capture this
intuition, we propose to represent videos by a hierarchy of mid-level action
elements (MAEs), where each MAE corresponds to an action-related spatiotemporal
segment in the video. We introduce an unsupervised method to generate this
representation from videos. Our method is capable of distinguishing
action-related segments from background segments and representing actions at
multiple spatiotemporal resolutions. Given a set of spatiotemporal segments
generated from the training data, we introduce a discriminative clustering
algorithm that automatically discovers MAEs at multiple levels of granularity.
We develop structured models that capture a rich set of spatial, temporal and
hierarchical relations among the segments, where the action label and multiple
levels of MAE labels are jointly inferred. The proposed model achieves
state-of-the-art performance in multiple action recognition benchmarks.
Moreover, we demonstrate the effectiveness of our model in real-world
applications such as action recognition in large-scale untrimmed videos and
action parsing
Learning action primitives for multi-level video event understanding
Human action categories exhibit significant intra-class variation. Changes in viewpoint, human appearance, and the temporal evolution of an action confound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categoriesof action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action recognition. Action primitives are discovered via a data-driven clustering approach that focuses on repeatable,discriminative sub-categories. Higher-level interactions between action primitives and the actions of a set of people present in a scene are learned. Empirical results demonstrate that these action primitives can be effectively localized, and using them to model action classesimproves action recognition performance on challenging datasets