Counting People Using Blobs and Contours

Counting People Using Blobs and Contours

Shafraz Subdurally (University of Mauritius, Réduit, Mauritius), Devin Dya (University of Mauritius, Réduit, Mauritius) and Sameerchand Pudaruth (University of Mauritius, Réduit, Mauritius)
Copyright: © 2013 |Pages: 16
DOI: 10.4018/ijcvip.2013040101
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Abstract

Counting the number of people in public locations has become imperative in surveillance applications for the good management of public space. The automatic counting of people can indeed help carry out the above tasks better and faster. In this paper, the authors propose two systems for counting people from images. Their proposed methods are based on the observation that heads are significantly more visible than any other features and are thus more easily distinguishable. The proposed systems use blobs and contour detection respectively to count the number of people. The results obtained from each system are very reliable. The average head detection rate of the systems is 82 and 84 percent respectively.
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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)

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