Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

State estimation of an unknown number of objects remains a challenging topic - despite the existence of theoretically bayes-optimal multi-object-filters - due to numerous assumptions in the modeling process. This thesis evaluates such filters in real multi-object-multi-sensor scenarios and proposes necessary extensions to existing models. The main application of the thesis is indoor pedestrian tracking.

Multi-Objekt-Verfolgung --- verteilte Systeme --- SensorenMulti-object-tracking --- sensors --- Objektklassifikation --- object classification --- pedestrian tracking --- distributed systems --- Personenverfolgung

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

State estimation of an unknown number of objects remains a challenging topic - despite the existence of theoretically bayes-optimal multi-object-filters - due to numerous assumptions in the modeling process. This thesis evaluates such filters in real multi-object-multi-sensor scenarios and proposes necessary extensions to existing models. The main application of the thesis is indoor pedestrian tracking.

Multi-Objekt-Verfolgung --- verteilte Systeme --- SensorenMulti-object-tracking --- sensors --- Objektklassifikation --- object classification --- pedestrian tracking --- distributed systems --- Personenverfolgung