Hybrid video surveillance systems using P300 based computational cognitive threat signature library

Comprehensive Integrated Border Management System (CIBMS) is aimed to reduce the infiltration at borders of countries and to overcome risk. It is an integration of manpower, sensor, networks, intelligence, command and control solutions. This research work is intended to create a reference library on threat signature using cognitive technology which will enhance the intelligence for CIBMS to reduce the human effort using cognitive science through Brain Computer Interface (BCI) application. The system proposes to use an Electroencephalogram (EEG) cap to monitor the operators brain signals when operator see any abnormal activity across border area and then records the exact video frame when the observer detects a threat. The combination of cognitive algorithm and EEG filtering not only reduces false alarms, it also helps operators to detect signs of threats that would be overlooked, such as flying birds, swaying branches or non threat object according the action recognition. This research aims to suggest a better optimization technique on P300 brain signals and build an operational library of threat signature which can be used for future automated surveillance system with more accuracy.