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A compact optical flowbased motion representation for real-time action recognition in surveillance scenes

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3 Author(s)
Shiquan Wang ; Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China ; Kaiqi Huang ; Tieniu Tan

We address the problem of action recognition. Our aim is to recognize single person activities in surveillance scenes. To meet the requirements of real scene action recognition, we present a compact motion representation for human activity recognition. With the employment of efficient features extracted from optical flow as the main part, together with global information, our motion representation is compact and discriminative. We also build a novel human action dataset(CASIA) in surveillance scene with three vertically different viewpoints and distant people. Experiments on CASIA dataset and WEIZMANN dataset show that our method can achieve satisfying recognition performance with low computational cost as well as robustness against both horizontal(panning) and vertical(tilting) viewpoint changes.

Published in:

Image Processing (ICIP), 2009 16th IEEE International Conference on

Date of Conference:

7-10 Nov. 2009