Role of Domain Knowledge and Sensor Quality in Robust Localization of Moving Objects

Robust identification and localization of moving objects depends on the quality of sensors, sensor coverage and the fusion of the information obtained from the sensors. Sensor fusion algorithms use domain and context knowledge to calculate the most plausible interpretation of all available data. Little research has been done on the trade-offs between these factors. This paper presents an empirical study of these trade-offs for a sensor fusion algorithm BBP based on Bayesian forward and backward propagation. To test the robustness of this algorithm under various conditions, we created “virtual sensors” whose performance characteristics were based on the data gathered by tracking 31 people in 112 locations using a set of 34 cameras and 91 badge readers. Our Monte Carlo simulations show that given good domain knowledge, BBP performs robustly even in the presence of poor sensors, thus providing a promising alternative to the expensive practice of installing better sensors and calibration procedures in order to improve surveillance systems.