Human detection based on curvelet transform and integrating heterogeneous features

Hong Han; Youjian Fan

doi:10.1117/12.883644

19 May 2011 Human detection based on curvelet transform and integrating heterogeneous features

Hong Han, Youjian Fan

Proceedings Volume 8049, Automatic Target Recognition XXI; 80490L (2011) https://doi.org/10.1117/12.883644
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

A method in Curvelet transformation and integrating heterogeneous features for human detection are proposed in this paper. The descriptor based on the second generation Curvelet transform (CTD) was proposed firstly, it concatenated the edge and texture feature vectors. To capture edge features, the statistic measures such as energy, entropy, standard deviation, max value and contrast computed from the blocks which is partitioned from the sub-bands of all the scales are concatenated. To get texture features, the lowest frequency sub-band coefficients were partitioned into overlapped blocks. Four co-occurrence matrixes were computed for each block. And some descriptors such as angular second-moment, contrast, correlation, sum of variance, sum of average and entropy are computed from the co-occurrence matrix, which are concatenated as the texture feature vector. And then the method integrating three feature extraction methods, such as Histogram of Oriented Gradient (HOG), Granularity-tunable Gradients Partition descriptors (GGP), and CTD, is proposed for human detection. Computational Cost Normalized classification Margin is used to determine the order of the feature to be evaluated. The experimental results on the basis of INRIA and MIT human database showed that CTD and integrating heterogeneous feature method increased the detection accuracy comparing to HOG and GGP.

Citation Download Citation

Hong Han and Youjian Fan "Human detection based on curvelet transform and integrating heterogeneous features", Proc. SPIE 8049, Automatic Target Recognition XXI, 80490L (19 May 2011); https://doi.org/10.1117/12.883644

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KEYWORDS

Feature extraction

Principal component analysis

Image fusion

Databases

Edge detection

Image classification

Matrices

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