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Technical Program
Paper Detail
| Paper: | WA-L6.7 |
| Session: | Knowledge-Based Image Processing For Classification And Recognition In Surveillance Applications |
| Time: | Wednesday, October 11, 12:00 - 12:20 |
| Presentation: |
Special Session Lecture
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| Title: |
TOWARDS A ROBUST SOLUTION TO PEOPLE COUNTING |
| Authors: |
Hasan Celik; Delft University of Technology / Philips Research Eindhoven | | | | | Alan Hanjalic; Delft University of Technology | | | | | Emile A. Hendriks; Delft University of Technology | | |
| Abstract: |
ABSTRACT Estimating the number of persons in a public place provides useful information for video-based surveillance and monitoring applications. In the case of oblique camera setup, counting is either achieved by detecting individuals or by statistically establishing relations between values of simple image features (e.g. amount of moving pixels, edge density, etc.) to the number of people. While the methods of the first category exhibit poor accuracy in cases of occlusions, the second category of methods are sensitive to perspective distortions, and require people to move in order to be counted. In this paper we investigate the possibilities of developing a robust statistical method for people counting. To maximize its applicability scope, we choose – in contrast to the majority of existing methods from this category – not to require prior learning of categories corresponding to different number of people. Second, we search for a suitable way of correcting the perspective distortion. Finally, we link the estimation to a confidence value that takes into account the known factors being of influence on the result. The confidence is then used to refine final results. Keywords: Automated surveillance, people counting |
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