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ACCV
2009
Springer

Extracting Spatio-temporal Local Features Considering Consecutiveness of Motions

9 years 4 months ago
Extracting Spatio-temporal Local Features Considering Consecutiveness of Motions
Recently spatio-temporal local features have been proposed as image features to recognize events or human actions in videos. In this paper, we propose yet another local spatio-temporal feature based on the SURF detector, which is a lightweight local feature. Our method consists of two parts: extracting visual features and extracting motion features. First, we select candidate points based on the SURF detector. Next, we calculate motion features at each point with local temporal units divided in order to consider consecutiveness of motions. Since our proposed feature is intended to be robust to rotation, we rotate optical flow vectors to the main direction of extracted SURF features. In the experiments, we evaluate the proposed spatio-temporal local feature with the common dataset containing six kinds of simple human actions. As the result, the accuracy achieves 86%, which is almost equivalent to stateof-the-art. In addition, we make experiments to classify large amounts of Web video c...
Akitsugu Noguchi, Keiji Yanai
Added 23 Jul 2010
Updated 23 Jul 2010
Type Conference
Year 2009
Where ACCV
Authors Akitsugu Noguchi, Keiji Yanai
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