Negative space template: a novel feature to describe activities in video

Rahman, Atiqur Shah, Song, I., and Leung, M.K.H. (2012) Negative space template: a novel feature to describe activities in video. In: Proceedings of the 2012 International Joint Conference on Neural Networks. 6252666. pp. 1-7. From: 2012 International Joint Conference on Neural Networks (IJCNN), 10-15 June 2012, Brisbane, Australia.

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Abstract

In this paper, we propose an implicit silhouette based (template based) method of recognizing activities in video. The novelty of the proposed method is that it extracts features from the negative space to form action templates while other implicit methods employed positive spaces to construct action templates. Extracting features from negative space facilitates the system to extract simple, yet effective features to describe each action. These features are robust to deformed actions due to complex boundary variations, partial occlusions, non-rigid deformation and small shadow. Moreover, other template based methods need to apply additional processing to reduce the dimensionality of the extracted features, but negative space templates do not require dimensionality reduction since it describes actions with low-dimensional features. The proposed method improves its processing time while achieving comparable accuracies with other state of the art methods.

Item ID: 23485
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4673-1489-3
ISSN: 2161-4393
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Date Deposited: 14 Dec 2012 02:36
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision @ 80%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 20%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8904 Media Services > 890401 Animation and Computer Generated Imagery Services @ 80%
89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 20%
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