Dense crowd counting based on perspective weight model using a fisheye camera

Abstract

Crowd monitoring in public has become an important and challenging topic in the field of automatic surveillance system. Existing approaches for crowd monitoring cannot obtain desired results in dense crowd places, which have the limitation of small field-of-view. This paper proposes a novel approach for dense crowd counting in public using a fisheye camera. To segment the crowd from the background, the methods of setting region of interest and subtracting background are firstly proposed. In the process of feature extraction, a perspective weight model is generated according to the perspective of the fisheye image, and a novel mathematical morphology method for fisheye images is developed for feature enhancement. Some features for describing the crowd number are extracted based on the proposed model and the novel mathematical morphology method. Finally, the crowd is counted using three different classifiers including the multiple linear regression, the backpropagation neural network and the regression trees for comparison based on the extracted features. The experimental results show that the proposed approach is effective for dense crowd counting in fisheye images.