Counting moving persons in crowded scenes
The paper presents a method for estimating the number of moving people in a scene for video surveillance applications. The method performance has been characterized on the public database used for the PETS 2009 and 2010 international competitions; the proposed method has been compared, on the same database, with the PETS competitions participants. The system exhibits a high accuracy, and revealed to be so fast that it can be used in real time surveillance applications. The rationale of the method lies on the extraction of suited scale-invariant feature points and the successive selection among them of the moving ones, under the hypothesis that the latter are associated to moving people. The perspective distortions are taken into account by dividing the input frames into smaller horizontal zones, each having (approximately) the same perspective effects. Therefore, the evaluation of the number of people is separately carried out for each zone, and the results are summed up. The most important peculiarity of the proposed method is the availability of a simple training procedure using a brief video sequence that shows a person walking around in the scene; the procedure automatically evaluates all the parameters needed by the system, thus making the method particularly suited for end-user applications.
people counting crowd density estimation video interpretation and understading