Statistical Analysis of Dynamic Actions

Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters and necessitate as short as possible learning stage. In this paper we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is non-parametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents.

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Lihi Zelnik-Manor, Michal Irani
• Keywords: Distance Measure, Multiple Temporal Scales, PAMI 2006, Real-world Action Recognition