An algorithm to correct for camera vibrations in optical motion tracking systems
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The purpose of this paper is to demonstrate the ultrasound tracking strategy for the acoustically actuated bubble-based microswimmer.The ultrasound tracking performance is evaluated by comparing the tracking results with the camera tracking. A benchtop experiment is conducted to capture the motion of two types of microswimmers by synchronized ultrasound and camera systems. A laboratory developed tracking algorithm is utilized to estimate the trajectory for both tracking methods.The trajectory reconstructed from ultrasound tracking method compares well with the conventional camera tracking, exhibiting a high accuracy and robustness for three different types of moving trajectories.Ultrasound tracking is an accurate and reliable approach to track the motion of the acoustically actuated microswimmers.Ultrasound imaging is a promising candidate for noninvasively tracking the motion of microswimmers inside the body in biomedical applications and may further promote the real-time control strategy for the microswimmers.
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This paper presents a physical model-based method for recovering and tracking nonrigid motion of elastic objects. The proposed method recovers the motion in terms of actual physical parameters (Young's modulus) that characterize the dynamics of the objects. The tracking scheme synthesizes the motion of the points inside the object from the boundary observations, constrained by the physical parameters. Experiments on three image sequences show that using the recovered physical parameters as constraints can greatly improve the tracking quality.
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In this paper, the study for computer vision in motion capturing and motion replication is developed. A single web-cam is employed for motion capture, the 3D virtual character model mimicked the user movements, and some specific user's movement can trigger different animations (such as aggressive and defensive). Several proposed methods for motion capture and motion representation are described in this paper. The implemented project can be categorised into motion tracking, motion mimicking, motion analysis, and motion representation. Each stage has illustrated some simple outcomes as it is proposed in this paper. Further development and improvement for these sections are recommended.
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We review methods for kinematic tracking of the human body in video. The review is part of a projected book that is intended to cross-fertilize ideas about motion representation between the animation and computer vision communities. The review confines itself to the earlier stages of motion, focusing on tracking and motion synthesis; future material will cover activity representation and motion generation.
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Several approaches for tracking the movement of objects in 3D space exist. Most of them actually track the motion of special sensors attached to the object of interest. This method is often not appropriate, because it obstructs the free movement of the object. However, some localization approaches depend only on natural properties of objects and don't require any special hardware. In this paper we present the overview of an optical tracking system that uses stereoscopic camera to detect motion. To estimate its accuracy we compare the results with reference measurements made by magnetic tracking device. We report results of several experiments and present the main factors that contribute to the total tracking error. The average difference between the two tracking systems is 1.7 cm. In the end we present a few improvements that could further reduce the tracking error.
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The distinction of translational and rotational camera motion and the recognition of moving objects is an important topic for scientific film studies. In this paper, we present an approach to distinguish between camera and object motion in MPEG videos and provide a pixel-accurate segmentation of moving objects. Compressed domain features are used as far as possible in order to reduce computation time. First, camera motion parameters are estimated and translational movements are distinguished from rotational movements based on a three-dimensional (3D) camera model. Then, motion vectors which do not fit to the camera motion estimate are assigned to object clusters. The moving object information is utilized to refine the camera motion estimate, and a novel compressed domain tracking algorithm is applied to verify the temporal consistency of detected objects. In contrast to previous approaches, the tracking of both moving objects and background allows to perform their separation iteratively only once per shot. The object boundary is estimated with pixel accuracy via active contour models. Experimental results demonstrate the feasibility of the proposed algorithm.
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Patient motion, which causes artifacts in reconstructed images, can be a serious problem in SPECT imaging. If patient motion can be detected and quantified, the reconstruction algorithm can compensate for the motion. Most previous approaches to detecting patient motion have relied on only the acquired projection data, using, for example, consistency checks or motion-tracking to detect motion. Our approach is based on optical tracking of the patient using a pair of web cameras to acquire stereo images. The stereo images are analyzed by a visual tracking system (VTS) that detects changes in the stereo images over time to track locations on the patient surface. Patient surface motion can then be used to infer motion within the patient body, which will be used to correct for patient motion. The system consists of a three-headed SPECT system and two web cameras connected to a PC.
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The proliferation of AR/MR/XR technology continues to grow. However, with few exceptions, the advances in motion tracking to support placement of symbols and images on the real world scene have not kept up. In order for AR/MR/XR to move from narrow niche applications to more general use, motion tracking providing accurate pose will also need to work in diverse environments and conditions. Many additional factors comprise tracking performance, but they are more easily addressed. This paper uses several real-world, dismounted and mounted scenarios to determine the minimum tracking accuracy needed to achieve success. It then proposes a minimum performance specification for motion tracking to support general AR/MR/XR displays.
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Abstract This paper presents an adapted method for tracking camera motion in the visualization of augmented reality. A tracking algorithm based on significant points and descriptors without the use of optical flow is described. The tracking module is written in C++. Experiments have been carried out of the precision of the system using the camera motion-tracking module created.
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