Abstract: This paper presents a method to count people for video surveillance applications. The proposed method adopts the indirect approach, according to which the number of persons in the scene is inferred from the value of some easily detectable scene features. In particular, the proposed method first detects the SURF interest points associated to moving people, then determines the number of persons in the scene by a weigthed sum of the SURF points. In order to take into account the fact that, due to the perspective, the number of points per person tends to decrease the farther the person is from the camera, the weight attributed to each point depends on its coordinates in the image plane. In the design of the method, particular attention has been paid in order to obtain a system that can be easily deployed and configured. In the experimental evaluation, the method has been extensively compared with the algorithms by Albiol et al. and by Conte et al., which both adopt a similar approach. The experimentations have been carried out on the PETS 2009 dataset and the results show that the proposed method obtains a high value of the accuracy.
An Effective Method For Counting People in Video-surveillance Applications
CONTE, Donatello;FOGGIA, PASQUALE;PERCANNELLA, Gennaro;TUFANO, FRANCESCO;VENTO, Mario
2011-01-01
Abstract
Abstract: This paper presents a method to count people for video surveillance applications. The proposed method adopts the indirect approach, according to which the number of persons in the scene is inferred from the value of some easily detectable scene features. In particular, the proposed method first detects the SURF interest points associated to moving people, then determines the number of persons in the scene by a weigthed sum of the SURF points. In order to take into account the fact that, due to the perspective, the number of points per person tends to decrease the farther the person is from the camera, the weight attributed to each point depends on its coordinates in the image plane. In the design of the method, particular attention has been paid in order to obtain a system that can be easily deployed and configured. In the experimental evaluation, the method has been extensively compared with the algorithms by Albiol et al. and by Conte et al., which both adopt a similar approach. The experimentations have been carried out on the PETS 2009 dataset and the results show that the proposed method obtains a high value of the accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
