Counting People Using Blobs and Contours

2. Literature Review

In Dan Kong (2006), a learning-based method for counting pedestrians in crowds has been proposed. The system uses edge orientation and blob size histograms. This is done by applying background subtraction and edge detection to each frame. The features are then extracted (Figure 1).

Figure 1.

Features of Pedestrians: (a) original image, (b) foreground mask image (c) edge detection map, (d) the edge map after the ‘AND’ operation between (b) and (c)

The points in the image and the point on a 3D plane are related by a plane perspective transformation called homography. Having obtained the features, a homography is calculated between the ground plane and the image plane coordinates for the region of interest (ROI). For feature normalization, a density map measuring the relative size of persons and a global scale measuring camera orientation have been used (Figure 2).

Figure 2.

Region of interest in the image and density map (weights)