Periodic Pattern Detection for Real-Time Application

Abstract. Digital video stabilization approaches typically degrade their performances in presence of periodic patterns. Any kind of matching between consecutive frames is not usually able to work in presence of these kind of signals: the motion estimation engine is deceived and its performances degrade abruptly. In this paper we propose a fast fuzzy classifier able to recognize periodic and aperiodic pattern in the images that takes into account the peculiarities of digital video stabilization. Finally, the proposed classifier can be used as a filtering module in a block based video stabilization approach. Key words: Video Stabilization, periodic pattern, fuzzy classifier

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 nonparametric 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