Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors

Understand

We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation.

• A given video (containing several consecutive actions) is processed via a sequence of overlapping temporal windows.
• Each frame in a temporal window is represented through selective low-level spatio-temporal features which efficiently capture relevant local dynamics.
• Features from each window are represented as a Fisher vector, which captures first and second order statistics.