Complex threat detection: Learning vs. rules, using a hierarchy of features

Theft of cargo from a truck or attacks against the driver are threats hindering the day to day operations of trucking companies. In this work we consider a system, which is using surveillance cameras mounted on the truck to provide an early warning for such evolving threats. Low-level processing involves tracking people and calculating motion features. Intermediate-level processing provides kinematics and localisation, activity descriptions and threat stage estimates. At the high level, we compare threat detection performed with a statistical trained SVM based classifier against a rule based system. Results are promising, and show that the best system depends on the scenario.