A novel segmentation method for object tracking and recognition

Abstract

The increasing demand for the protection of persons and facilities requires the application of sophisticated technologies for surveillance and object tracking. 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. In this context there is a requirement of automatic and semi-automatic methods supporting the human observer in his decision-making process. A prevalent task is automatic tracking of striking objects like vehicles or individual persons in an image sequence during a time slice. Classical methods are based on template matching implying certain shortcomings concerning homogeneous background or passing objects occluding the target object. The authors propose a new concept for generating templates for IR target signatures based on the interpretation of laser range data in order to optimize the tracking process. The testbed is realized by a helicopter equipped with a multisensor suite (laser radar, imaging IR, GPS, IMU). Results are demonstrated by the analysis of an exemplary data set. A vehicle situated in a complex scenario is acquired by a forward moving sensor platform and is tracked robustly by the proposed method

Generation of efficient 2D templates from 3D multisensor data for correlation-based target tracking

“…Here the correlation was performed at every possible image position, compare [10]. It is possible to restrict the search region, the current position of the object in the image is known if it hasn't moved too much.…”

“…The analysis of this correlation matrix is carried out by comparing the local maxima (Fig. 8) as described in [10]. Fig.…”