Higher-order SVD analysis for crowd density estimation

This paper proposes a new method to estimate the crowd density based on the combination of higher-order singular value decomposition (HOSVD) and support vector machine (SVM). We first construct a higher-order tensor with all the images in the training set, and apply HOSVD to obtain a small set of orthonormal basis tensors that can span the principal subspace for all the training images. The coordinate, which best describes an image under this set of orthonormal basis tensors, is computed as the density character vector. Furthermore, a multi-class SVM classifier is designed to classify the extracted density character vectors into different density levels. Compared with traditional methods, we can make significant improvements to crowd density estimation. The experimental results show that the accuracy of our method achieves 96.33%, in which the misclassified images are all concentrated in their neighboring categories.

► This paper proposes a novel method for the estimation of crowd density.
► We combine both higher-order SVD analysis and support vector machine.
► Thanks to tensor, crowd feature is extracted from image preserving its native form.
► We build a more reasonable crowd database to evaluate our method.
► The accuracy achieves 96.33 which is about 12% higher than others.