Generation of efficient 2D templates from 3D multisensor data for correlation-based target tracking
The general demand for the prevention of collateral damages in military operations requires methods of robust automatic identification of target objects like vehicles especially during target approach. This requires the development of sophisticated techniques for automatic and semi-automatic interpretation of sensor data. In particular the automatic pre-analysis of reconnaissance data is important for the human observer as well as for autonomous systems. In the phase of target approach fully automatic methods are needed for the recognition of predefined objects. For this purpose appropriate sensors are used like imaging IR sensors suitable for day/night operation and laser radar supplying 3D information of the scenario. Classical methods for target recognition based on comparison with synthetic IR object models imply certain shortcomings, e.g. unknown weather conditions and the engine status of vehicles. We propose a concept of generating efficient 2D templates for IR target signatures based on the evaluation of a precise 3D model of the target generated from real multisensor data. This model is created from near-term laser range and IR data gathered by reconnaissance in advance to gain realistic and up-to-date target signatures. It consists of the visible part of the object surface textured with measured infrared values. This enables recognition from slightly differing viewing angles. Our test bed is realized by a helicopter equipped with a multisensor suite (laser radar, imaging IR, GPS, and IMU). Results are demonstrated by the analysis of a complex scenario with different vehicles.