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ICPR
2008
IEEE

People detection based on co-occurrence of appearance and spatiotemporal features

14 years 4 months ago
People detection based on co-occurrence of appearance and spatiotemporal features
This paper presents a method for detecting people based on the co-occurrence of appearance and spatiotemporal features. Histograms of oriented gradients(HOG) are used as appearance features, and the results of pixel state analysis are used as spatiotemporal features. The pixel state analysis classifies foreground pixels as either stationary or transient. The appearance and spatiotemporal features are projected into subspaces in order to reduce the dimensions of the vectors by principal component analysis(PCA). The cascade AdaBoost classifier is used to represent the cooccurrence of the appearance and spatiotemporal features. The use of feature co-occurrence, which captures the similarity of appearance, motion, and spatial information within the people class, makes it an effective detector. Experimental results show that the performance of our method is about 29% better than that of the conventional method.
Bon-Woo Hwang, Hironobu Fujiyoshi, Takeo Kanade, Y
Added 05 Nov 2009
Updated 05 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Bon-Woo Hwang, Hironobu Fujiyoshi, Takeo Kanade, Yuji Yamauchi
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