Efficient human situation recognition using Sequential Monte Carlo in discrete state spaces

This dissertation analyses these challenges and provides solutions for SMC methods. The large, categorical and causal state-space is the largest factor for the inefficiency of current SMC methods. The marginal filter is analysed in detail for its advantages in categorical states over the particle filter. An optimal pruning strategy for the marginal filter is derived that limits the number of samples.Diese Dissertation analysiert diese Herausforderungen und entwickelt Lösungen für SMC-Methoden. Der große, kategorische und kausale Zustandsraum ist der größte Faktor für die Ineffizienz von aktuellen SMC-Methoden. Die Vorteile des Marginalen Filters in kategorischen Zustandsräumen gegenüber dem Partikelfilter werden detailliert analysiert. Eine optimale Pruning-Strategie wird für den Marginal Filter entwickelt

Activity, context, and plan recognition with computational causal behavior models

Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced