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Descriptive temporal template features for visual motion recognition

By Hongying Meng and Nick Pears

Abstract

In this paper, a human action recognition system is proposed. The system is based on new, descriptive `temporal template' features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well known `Motion History Image' (MHI) temporal template are addressed and a new `Motion History Histogram' (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations

Topics: G700 Artificial Intelligence, G440 Human-computer Interaction, G740 Computer Vision
Publisher: Elsevier
Year: 2009
DOI identifier: 10.1016/j.patrec.2009.03.003
OAI identifier: oai:eprints.lincoln.ac.uk:1976

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