NDLI: Granularity-tunable gradients partition (GGP) descriptors for human detection

Content Provider	IEEE Xplore Digital Library
Author	Yazhou Liu Shiguang Shan Wenchao Zhang Xilin Chen Wen Gao
Copyright Year	2009
Description	Author affiliation: Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing, China (Shiguang Shan; Xilin Chen) \|\| Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China (Yazhou Liu; Wenchao Zhang) \|\| Inst. of Digital Media, Peking Univ., Beijing, China (Wen Gao)
Abstract	This paper proposes a novel descriptor, granularity-tunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular uncertainty of the line segments in the Hough space. Then this uncertainty is backprojected into the image space by orientation-space partitioning to achieve efficient implementation. By changing the granularity parameter, the level of uncertainty can be controlled quantitatively. Therefore a family of descriptors with versatile representation property can be generated. Specifically, the finely granular GGP descriptors can represent the specific geometry information of the object (the same as Edgelet); while the coarsely granular GGP descriptors can provide the statistical representation of the object (the same as histograms of oriented gradients, HOG). Moreover, the position, orientation, strength and distribution of the gradients are embedded into a unified descriptor to further improve the GGP's representation power. A cascade structured classifier is built by boosting the linear regression functions. Experimental results on INRIA dataset show that the proposed method achieves comparable results to those of the state-of-the-art methods.
Starting Page	1255
Ending Page	1262
File Size	874302
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206724
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Shape Uncertainty Histograms Support vector machines Support vector machine classification Boosting Detectors Covariance matrix Noise robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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