A crowd flow estimation method based on dynamic texture and GRNN

Abstract

To overcome the deficiencies of the existing methods used in the estimation of the crowd flow with high-density and multi-motion direction, a crowd flow estimation method based on dynamic texture and generalized regression neural network (GRNN) is presented in this paper. The method firstly extracts the dynamic texture features through optical flow, performs the moving crowd segmentation by the dynamic texture features and level set algorithm to achieve ROIs, and then the regression analysis based on GRNN between ROI features and crowd flow is adopted to achieve the real-time crowd flow estimation results in the crowd scene. Experimental results show that the proposed crowd flow estimation algorithm is more suitable than the existing methods to the crowd flow estimation applications with low complexity, high accuracy and high real-time requirements.