Camera Tracking using natural feature point matching for Augmented Reality
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Augmented reality (AR) environments are suffering from a limited workspace. In addition, registration issues are also increased by the use of a mobile camera on the user that provides a first-person perspective (1PP). Using several fixed cameras reduces th ...
Video-based camera tracking consists in trailing the three dimensional pose followed by a mobile camera using video as sole input. In order to estimate the pose of a camera with respect to a real scene, one or more three dimensional references are needed. ...
Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some appro ...
Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, ...
We present a method that is able to track several 3D objects si- multaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale we ...
This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and ...
This paper presents our participation in the CLEAR 07 evaluation workshop head pose estimation tasks where two head pose estimation tasks were to be addressed. The first task estimates head poses with respect to (w.r.t.) a single camera capturing people se ...
This work tackles the challenge of detecting and matching objects in scenes observed simultaneously by fixed and mobile cameras. No calibration between the cameras is needed, and no training data is used. A fully automated system is presented to detect if ...
In this paper a Multi-Level Feature Matching (MLFM) method is presented for 3D hand posture reconstruction of a virtual keyboard system. The human hand is modeled with a mixture of different levels of detail, from skeletal to polygonal surface representati ...
In this paper a Multi-Level Feature Matching (MLFM) method is presented for 3D hand posture reconstruction of a virtual keyboard system. The human hand is modeled with a mixture of different levels of detail, from skeletal to polygonal surface representati ...
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