18,357 research outputs found
Vision-Based Production of Personalized Video
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach
On Rendering Synthetic Images for Training an Object Detector
We propose a novel approach to synthesizing images that are effective for
training object detectors. Starting from a small set of real images, our
algorithm estimates the rendering parameters required to synthesize similar
images given a coarse 3D model of the target object. These parameters can then
be reused to generate an unlimited number of training images of the object of
interest in arbitrary 3D poses, which can then be used to increase
classification performances.
A key insight of our approach is that the synthetically generated images
should be similar to real images, not in terms of image quality, but rather in
terms of features used during the detector training. We show in the context of
drone, plane, and car detection that using such synthetically generated images
yields significantly better performances than simply perturbing real images or
even synthesizing images in such way that they look very realistic, as is often
done when only limited amounts of training data are available
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to anatomically plausible as well as temporally smooth hand motions. For
training our CNN we propose a novel approach for the synthetic generation of
training data that is based on a geometrically consistent image-to-image
translation network. To be more specific, we use a neural network that
translates synthetic images to "real" images, such that the so-generated images
follow the same statistical distribution as real-world hand images. For
training this translation network we combine an adversarial loss and a
cycle-consistency loss with a geometric consistency loss in order to preserve
geometric properties (such as hand pose) during translation. We demonstrate
that our hand tracking system outperforms the current state-of-the-art on
challenging RGB-only footage
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