Incremental Learning with Soft-Biometric Features for People Re-Identification in Multi-Camera Environments

In this paper, a solution for the appearance based people re-identification problem in a non-overlapping multicamera surveillance environment is presented. For this purpose, an incremental learning approach and a SVM classifier have been considered. The proposed methods update the appearance model across different camera conditions in three different ways: based on time lapses, on change of camera and on the automatic selection of the most representative samples. In order to test the proposed methods, a complete database was acquired at Barajas international airport (the MUBA proposed database). Further the well known PETS 2006 and PETS 2009 databases were considered. The system has been designed for video surveillance security. The main idea of this system is that, in an initial point, the suspect is manually identified by the user. Then, from that moment, the system is able to identify the selected subject across the different cameras in the surveillance area. The results obtained show the importance of the model update and the huge potential of the incremental learning approach.