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.