Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call security personnel in case of anomalous events in the surveillance area. We are describing the concept and the realization of an indoor security assistance system for realtime decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified and potential threats are localized inside the demonstrator. Data for person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision information and the results of the person classification affords the localization of threats and a timely reaction of security personnel.
A Security Assistance System Combining Person Tracking with Chemical Attributes and Video Event Analysis
FORESTI, Gian Luca;MICHELONI, Christian;PICIARELLI, Claudio;
2008-01-01
Abstract
Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call security personnel in case of anomalous events in the surveillance area. We are describing the concept and the realization of an indoor security assistance system for realtime decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified and potential threats are localized inside the demonstrator. Data for person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision information and the results of the person classification affords the localization of threats and a timely reaction of security personnel.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
