Crowd counting using statistical features based on curvelet frame change detection

Abstract

Automatic counting for moving crowds in digital images is an important application in computer artificial intelligence, especially for safety and management purposes. This paper presents a new method to estimate the size of a crowd. The new algorithm depends on sequential frame differences to estimate the crowd size in a scene. However, relying only on these simple differences adds more constraints for extracting sufficient crowd descriptors. A curvelet transform is employed to achieve that goal. Every two sequential frames are transformed into multi-resolution and multi-direction formats, and then the frame differences are detected at every subband in the curvelet domain. Statistical features out of each subband are then calculated, and the collected features from all subbands are considered as a descriptor vector for the crowd in the scene. Finally, a neural network is manipulated to map the descriptor vectors into predicted counts. The experimental results show that the proposed curvelet statistical features are more robust and provide crowd counting with higher accuracy than previous approaches.