RFID-based hybrid Camera Tracking in Virtual Studio

This paper addresses the problem of Camera tracking in virtual studio environment. The traditional camera tracking methods can be classified into optical-based or electromechanical sensor-based. However, the electromechanical method is extensive time-consuming calibration procedures and cost too much; the optical method suffers from the error detection of references features and the chorma keying limitation in virtual studio. Therefore, in order to overcome those problems, we proposed a novel RFID-based hybrid camera tracking method in virtual studio application. Firstly, we designed a RFID passive tags based camera tracker. By using the triangular position algorithm, the accuracy could reach up to 5 centimeters. Secondly, we combined the optical based tracking method into RFID tracker with the aim to improve the orientation and position accuracy. Finally, the experiment results showed that this method could be a novel potential solution for camera tracking system in virtual studio applications. Keywords-RFID, camera tracking, chorma key, SLA

Sensor-Based SLAM for Camera Tracking in Virtual Studio Environment

This paper addresses the problem of Camera Tracking in virtual studio environment. The traditional camera tracking methods are vision-based or sensor-based. However, the Chroma Keying process in virtual studio requires the color cues, such as blue screen, to segment objects from mages and videos. It limits the application of vision-based tracking methods in virtual studio since the background could not provide enough feature information. Therefore, in our research, we would try to apply the SLAM (simultaneously localization and mapping) methodology from mobile robots to the camera tracking area. We describe a sensor-based SLAM extension algorithm for 2D camera tracking in virtual studio. Also a technique call Map Adjustment is proposed to increase the accuracy and efficiency of the algorithm. The simulation results would be given in the conclusion. Keywords-SLAM, Particle Filter, Chroma Keying, Camera Trackin

Accelerated volumetric reconstruction from uncalibrated camera views

While both work with images, computer graphics and computer vision are inverse problems. Computer graphics starts traditionally with input geometric models and produces image sequences. Computer vision starts with input image sequences and produces geometric models. In the last few years, there has been a convergence of research to bridge the gap between the two fields. This convergence has produced a new field called Image-based Rendering and Modeling (IBMR). IBMR represents the effort of using the geometric information recovered from real images to generate new images with the hope that the synthesized ones appear photorealistic, as well as reducing the time spent on model creation. In this dissertation, the capturing, geometric and photometric aspects of an IBMR system are studied. A versatile framework was developed that enables the reconstruction of scenes from images acquired with a handheld digital camera. The proposed system targets applications in areas such as Computer Gaming and Virtual Reality, from a lowcost perspective. In the spirit of IBMR, the human operator is allowed to provide the high-level information, while underlying algorithms are used to perform low-level computational work. Conforming to the latest architecture trends, we propose a streaming voxel carving method, allowing a fast GPU-based processing on commodity hardware

Efficient, Causal Camera Tracking in Unprepared Environments

This paper addresses the problem of tracking the 3D pose of a camera in space, using the images it acquires while moving freely in unmodeled, arbitrary environments. A novel feature-based approach for camera tracking is proposed, intended to facilitate tracking in on-line, time-critical applications such as video see-through augmented reality. In contrast to several existing methods which are designed to operate in a batch, off-line mode, assuming that the whole video sequence to be tracked is available before tracking commences, the proposed method operates on images incrementally. At its core lies a feature-based 3D plane tracking technique, which permits the estimation of the homographies induced by a virtual 3D plane between successive image pairs. Knowledge of these homographies allows the corresponding projection matrices encoding camera motion to be expressed in a common projective frame and, therefore, to be recovered directly, without estimating 3D structure. Projective camera matrices are then upgraded to Euclidean and used for recovering structure, which is in turn employed for refining the projection matrices through local resectioning. The proposed approach is causal, is tolerant to erroneous and missing feature matches, does not require modifications of the environment and has computational requirements that permit a near real-time..