Detection of security related affect and behaviour in passenger transport

Abstract

Surveillance of drivers, pilots or passengers possesses significant potential for increased security within passenger transport. In an automotive setting the interaction can e.g. be improved by social awareness of an MMI. As further example security marshals can be efficiently positioned guided by according systems. Within this scope the detection of security relevant behavior patterns as aggressiveness or stress is discussed. The focus lies on real-life usage respecting online processing, subject independency, and noise robustness. The approach introduced employs multivariate time-series analysis for the synchronization and data reduction of audio and video by brute-force feature generation. By combined optimization of the large audiovisual space accuracy is boosted. Extensive results are reported on aviation behavior, as well as in particular for the audio channel on numerous standard corpora. The influence of noise will be discussed by representative carnoise overlay.