Tracking framework for heterogeneous sensor sources

This paper presents a scale-independent (space and time) tracking framework for moving entities (objects, animals, humans). The framework allows for the integration, processing and visualization of spatio-temporal data obtained from heterogeneous sensor sources. The framework does not rely upon one tracking and localization technology for the data collection, but instead considers the combination of several technologies to obtain and calculate the best positioning result. Not only are typical localization and tracking technologies considered as data providers, but also keystroke sensors, like touch screens with a fixed location in the tracking environment. Different sizes and characteristics of moving entities are considered as well as different tracking environments or use cases in different domains. A prototypical implementation of basic functionalities processes the spatio-temporal data in a core and provides it by different internal and external interfaces. The modular framework consists of different usage modules. A visualization module is implemented as a first module for the framework. Thus the display of an existing three-dimensional environmental model with the sensors and the visualization of the tracked entities are possible. Furthermore, sensors can be added, changed and managed in the three-dimensional scene. The development of further modules for data analysis for different use cases allows for the analysis of large amounts of data, for example with machine learning techniques. Two different use cases (laboratory mice and service technicians) were used to develop and apply the framework.