Abstract. In this paper we present a framework for a multi-touch surface using multiple cameras. With an overhead camera and a side-mounted camera we determine the fingertip positions. After determining the fundamental matrix that relates the two cameras, we calculate the three dimensional coordinates of the fingertips. The intersection of the epipolar lines from the overhead camera with the fingertips detected in the side camera image provides the fingertip height. Touches are detected when the measured fingertip height from the touch surface is zero. We interpret touch events as hand gestures which can be generalized into commands for manipulating applications. We offer an example application of a multi-touch finger painting program.
The NIST Construction Metrology and Automation Group (CMAG) has ongoing research in the application of laser imaging technologies in construction. Previous work has primarily focused on terrestrial LADAR scanning for applications such as terrain characterization, earthmoving analysis, and targeted object localization. The LADAR systems used in those research efforts can be characterized by long scan intervals and dense point cloud returns. Current research includes the investigation of a new class of commerciallyavailable, optical range image sensors for construction mobility applications. These flash LADAR systems (or 3D range cameras) yield a low resolution 3D range map and intensity image at up to 30 frames per second, and show promise for applications such as obstacle avoidance and object detection and tracking. This paper summarizes recent CMAG research with these sensors including calibration and performance evaluation. Future work involving crane obstacle avoidance and positioning is also discussed.
We present a method for tracking objects in 3D by comparing views from multiple video cameras against a 3D model. Camera-based object tracking is a vast field that until recently has been limited to the 2D (image) domain. With the falling cost of sensors in recent years, the research community has been shifting its focus to 3D tracking using networks of calibrated cameras. The standard approach has been to run traditional 2D tracking in each video and fuse the results. The algorithm described below tracks objects directly in 3D to give increased precision and to overcome limitations inherent in the standard approach. We demonstrate the effectiveness in tracking construction personnel in video sequences of a construction site mock-up, resulting in a reduction in tracking error over current methods.
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