Audio-Video Sensor Fusion for the Detection of Security Critical Events in Public Spaces

Increasing security concerns in the public domain lead to a more widespread use of video and audio surveillance techniques. While both technologies are already advanced, they still produce high false alarm rates when deployed on their own under realistic conditions. We present a method for sensor fusion based on weighted maps and a rule engine. The system was tested in a public space with the combination of audio localization, audio classification and video crowd detection, using 79 simulated security relevant scenarios and 44 hours sample data recorded over a period of several weeks. It has been shown that the false positive rate was reduced by 60 % and the localization accuracy has been increased by 25% with the fusion approach compared to the detection performance of individual sensors alone.