155,727 research outputs found
DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph
This paper describes DeepKey, an end-to-end deep neural architecture capable
of taking a digital RGB image of an 'everyday' scene containing a pin tumbler
key (e.g. lying on a table or carpet) and fully automatically inferring a
printable 3D key model. We report on the key detection performance and describe
how candidates can be transformed into physical prints. We show an example
opening a real-world lock. Our system is described in detail, providing a
breakdown of all components including key detection, pose normalisation,
bitting segmentation and 3D model inference. We provide an in-depth evaluation
and conclude by reflecting on limitations, applications, potential security
risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images
covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
Vehicle pose estimation using G-Net: multi-class localization and depth estimation
In this paper we present a new network architecture, called G-Net, for 3D pose estimation on RGB images which is trained in a weakly supervised manner. We introduce a two step pipeline based on region-based Convolutional neural networks (CNNs) for feature localization, bounding box refinement based on non-maximum-suppression and depth estimation. The G-Net is able to estimate the depth from single monocular images with a self-tuned loss function. The combination of this predicted depth and the presented two-step localization allows the extraction of the 3D pose of the object. We show in experiments that our method achieves good results compared to other state-of-the-art approaches which are trained in a fully supervised manner.Peer ReviewedPostprint (author's final draft
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in the
data that is induced by changes in light, background, partial occlusion as well
as the varying pose, texture, and shape of objects. Generative approaches to
computer vision allow us to overcome this difficulty by explicitly modeling the
physical image formation process. Using generative object models, the analysis
of an observed image is performed via Bayesian inference of the posterior
distribution. This conceptually simple approach tends to fail in practice
because of several difficulties stemming from sampling the posterior
distribution: high-dimensionality and multi-modality of the posterior
distribution as well as expensive simulation of the rendering process. The main
difficulty of sampling approaches in a computer vision context is choosing the
proposal distribution accurately so that maxima of the posterior are explored
early and the algorithm quickly converges to a valid image interpretation. In
this work, we propose to use a Bayesian Neural Network for estimating an image
dependent proposal distribution. Compared to a standard Gaussian random walk
proposal, this accelerates the sampler in finding regions of the posterior with
high value. In this way, we can significantly reduce the number of samples
needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201
- …