Kai Jüngling

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One of the main challenges in computer vision is the automatic detection of specific object classes in images. Recent advances of object detection performance in the visible spectrum encourage the application of these approaches to data beyond the visible spectrum. In this paper, we show the applicability of a well known, local-feature based object detector(More)
The automatic detection and tracking of pedestrians in imagery constitute important and challenging problems both in computer vision and driver assistance systems. We address these problems for the case of a forward looking monocular infrared camera under strong vehicle induced camera motion. An integrated detection & tracking strategy is introduced(More)
In this paper, we present an approach for person reidentification in multi-camera networks. This approach employs the Implicit Shape Model and SIFT features for person re-identification. One important property of the reidentification approach is that it is closely coupled to a person detection and tracking and uses SIFT feature models which are built during(More)
In surveillance applications human operators are either confronted with a high cognitive load or monotonic time periods where the operator’s attention rapidly decreases. Therefore, automatic high-level interpretation of image sequences gains increasing importance in assisting human operators. We present a generic hierarchical system that generates(More)
This paper introduces a generic architecture for the fusion of perceptual processes and its application in real-time object tracking. In this architecture, the well known anchoring approach is, by integrating techniques from information fusion, extended to multi-modal anchoring so as to be applicable in a multi-process environment. The system architecture(More)
A fundamental problem in computer vision is the precise determination of correspondences between pairs of images. Many methods have been proposed which work very well for image data from one modality. However, with the wide availability of sensor systems with different spectral sensitivities there is growing demand to automatically fuse the information from(More)
An important field in today’s computer vision is person centric video analysis. The basis of this person centric analysis is the detection and tracking of people in video data. In many cases it is not sufficient to track people when they continuously appear in the camera’s field of view, but to also reacquire a track after a person has left a field of view(More)
A well known problem in computer vision and photogrammetry is the precise online mapping of the surrounding scenery. Due to the nature of single projective sensor configurations with inherent 7-DoF, error accumulation and scale drift is still a problem for vision based systems. This is especially relevant for difficult motion trajectories. However, it is(More)