Dynamic classification for video stream using support vector machine

A dynamic classification using the support vector machine (SVM) technique is presented in this paper as a new `incremental' framework for multiple-classifying video stream data. The contribution of this study is the derivation of a unique, fast and simple to implement technique that allows multi-classification of behavioral motions based on an adaptation of the Least Square SVM (LS-SVM) formulation. This dynamic approach leads to an extension of SVM beyond its current static image-based learning methodologies. The proposed incremental multi-classification method is applied to video stream data, which consists of an articulated humanoid model monitored by a surveillance camera. The initial supervised off-line learning phase is followed by a visual behavior data acquisition and proposed incremental learning phase. The resulting error rate and the confidence level for the proposed technique demonstrate its validity and merits in articulated motion learning. Furthermore, the enabled ...

• Type: Journal
• Year: 2008
• Where: ASC
• Authors: Mariette Awad, Yuichi Motai
• Keywords: ASC 2006, ASC 2008, Incremental Learning Phase, Least Square SVM, Stream Data