28,661 research outputs found
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
3D Object Reconstruction from Hand-Object Interactions
Recent advances have enabled 3d object reconstruction approaches using a
single off-the-shelf RGB-D camera. Although these approaches are successful for
a wide range of object classes, they rely on stable and distinctive geometric
or texture features. Many objects like mechanical parts, toys, household or
decorative articles, however, are textureless and characterized by minimalistic
shapes that are simple and symmetric. Existing in-hand scanning systems and 3d
reconstruction techniques fail for such symmetric objects in the absence of
highly distinctive features. In this work, we show that extracting 3d hand
motion for in-hand scanning effectively facilitates the reconstruction of even
featureless and highly symmetric objects and we present an approach that fuses
the rich additional information of hands into a 3d reconstruction pipeline,
significantly contributing to the state-of-the-art of in-hand scanning.Comment: International Conference on Computer Vision (ICCV) 2015,
http://files.is.tue.mpg.de/dtzionas/In-Hand-Scannin
Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
This paper presents a method which can track and 3D reconstruct the non-rigid
surface motion of human performance using a moving RGB-D camera. 3D
reconstruction of marker-less human performance is a challenging problem due to
the large range of articulated motions and considerable non-rigid deformations.
Current approaches use local optimization for tracking. These methods need many
iterations to converge and may get stuck in local minima during sudden
articulated movements. We propose a puppet model-based tracking approach using
skeleton prior, which provides a better initialization for tracking articulated
movements. The proposed approach uses an aligned puppet model to estimate
correct correspondences for human performance capture. We also contribute a
synthetic dataset which provides ground truth locations for frame-by-frame
geometry and skeleton joints of human subjects. Experimental results show that
our approach is more robust when faced with sudden articulated motions, and
provides better 3D reconstruction compared to the existing state-of-the-art
approaches.Comment: Accepted in DICTA 201
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