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Online ISSN:1349-8606 |
| Progress in Informatics |
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| No.7 March 2010 |
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| Page 33-42 |
PDF(2,298KB) | References |
| doi:10.2201/NiiPi.2010.7.5 |
| People detection based on co-occurrence of appearance and spatio-temporal features |
| Yuji YAMAUCHI1, Hironobu FUJIYOSHI2, Yuji IWAHORI3, and Takeo KANADE4 |
1,2,3Graduate School of Engineering, Chubu University
4Robotics Institute, Carnegie Mellon University
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(Received: September 19, 2009)
(Revised: January 12, 2010)
(Accepted: January 13, 2010)
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Abstract:
This paper presents a method for detecting people based on co-occurrence of appearance and spatio-temporal features. In our method, 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 spatio-temporal features are projected into subspaces in order to reduce the dimension of feature vectors by principal component analysis (PCA). The cascade AdaBoost classifier is used to represent the co-occurrence of the appearance and spatio-temporal features. The use of feature co-occurrence, which captures the similarity of appearance, motion, and spatial information within the people class, makes it possible to construct an effective detector. Experimental results show that the performance of our method is about 29.0% better than that of the conventional method.
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Keywords:
People detection, histograms of oriented gradients, Pixel State Analysis, co-occurrence, AdaBoost
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| PDF(2,298KB) | References |
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