1,018 research outputs found
Understanding egocentric human actions with temporal decision forests
Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions.
The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces
DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated by hand-crafted features and boosted classifiers, potentially
losing performance due to suboptimal design choices. We propose a Convolutional
Neural Network (CNN) based detector for this task. We show how to effectively
apply CNNs for detection in 2D range data, and propose a depth preprocessing
step and voting scheme that significantly improve CNN performance. We
demonstrate our approach on wheelchairs and walkers, obtaining state of the art
detection results. Apart from the training data, none of our design choices
limits the detector to these two classes, though. We provide a ROS node for our
detector and release our dataset containing 464k laser scans, out of which 24k
were annotated.Comment: Lucas Beyer and Alexander Hermans contributed equall
Children, Humanoid Robots and Caregivers
This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development
A machine learning approach for digital image restoration
This paper illustrates the process of image restoration in the sense of detecting images within a scanned document such as a photo album or scrapbook. The primary use case of this research is to accelerate the cropping process for the employees of Cinetis, a company based in Martigny, Switzerland that specializes in the digitalization of old media formats. In this paper, we will first summarize the state of the art in this field of research. This will include explanations of various techniques and algorithms involved with feature and document detection used by various digital companies
Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier –typically a random forest– to predict the position of each input pixel on the object surface. These estimates are then used to define an energy function that is minimized w.r.t. the object pose. In this paper, we focus on the first stage of the problem and propose a novel classifier based on a depth-aware Convolutional Neural Network. This classifier is able to learn a scale-adaptive regression model that yields very accurate pixel-level predictions, allowing to finally estimate the pose using a simple RANSAC-based scheme, with no need to optimize complex ad hoc energy functions. Our experiments on publicly available datasets show that our approach achieves remarkable improvements over state-of-the-art methods.Peer ReviewedPostprint (author's final draft
Characterizing Objects in Images using Human Context
Humans have an unmatched capability of interpreting detailed information about existent objects by just looking at an image. Particularly, they can effortlessly perform the following tasks: 1) Localizing various objects in the image and 2) Assigning functionalities to the parts of localized objects. This dissertation addresses the problem of aiding vision systems accomplish these two goals. The first part of the dissertation concerns object detection in a Hough-based framework. To this end, the independence assumption between features is addressed by grouping them in a local neighborhood. We study the complementary nature of individual and grouped features and combine them to achieve improved performance. Further, we consider the challenging case of detecting small and medium sized household objects under human-object interactions. We first evaluate appearance based star and tree models. While the tree model is slightly better, appearance based methods continue to suffer due to deficiencies caused by human interactions. To this end, we successfully incorporate automatically extracted human pose as a form of context for object detection. The second part of the dissertation addresses the tedious process of manually annotating objects to train fully supervised detectors. We observe that videos of human-object interactions with activity labels can serve as weakly annotated examples of household objects. Since such objects cannot be localized only through appearance or motion, we propose a framework that includes human centric functionality to retrieve the common object. Designed to maximize data utility by detecting multiple instances of an object per video, the framework achieves performance comparable to its fully supervised counterpart. The final part of the dissertation concerns localizing functional regions or affordances within objects by casting the problem as that of semantic image segmentation. To this end, we introduce a dataset involving human-object interactions with strong i.e. pixel level and weak i.e. clickpoint and image level affordance annotations. We propose a framework that utilizes both forms of weak labels and demonstrate that efforts for weak annotation can be further optimized using human context
Context-driven Object Detection and Segmentation with Auxiliary Information
One fundamental problem in computer vision and robotics is to
localize objects of interest in an image. The task can either be
formulated as an object detection problem if the objects are
described by a set of pose parameters, or an object segmentation
one if we recover object boundary precisely. A key issue in
object detection and segmentation concerns exploiting the spatial
context, as local evidence is often insufficient to determine
object pose in the presence of heavy occlusions or large object
appearance variations. This thesis addresses the object detection
and segmentation problem in such adverse conditions with
auxiliary depth data provided by RGBD cameras. We focus on four
main issues in context-aware object detection and segmentation:
1) what are the effective context representations? 2) how can we
work with limited and imperfect depth data? 3) how to design
depth-aware features and integrate depth cues into conventional
visual inference tasks? 4) how to make use of unlabeled data to
relax the labeling requirements for training data?
We discuss three object detection and segmentation scenarios
based on varying amounts of available auxiliary information. In
the first case, depth data are available for model training but
not available for testing. We propose a structured Hough voting
method for detecting objects with heavy occlusion in indoor
environments, in which we extend the Hough hypothesis space to
include both the object's location, and its visibility pattern.
We design a new score function that accumulates votes for object
detection and occlusion prediction. In addition, we explore the
correlation between objects and their environment, building a
depth-encoded object-context model based on RGBD data. In the
second case, we address the problem of localizing glass objects
with noisy and incomplete depth data. Our method integrates the
intensity and depth information from a single view point, and
builds a Markov Random Field that predicts glass boundary and
region jointly. In addition, we propose a nonparametric,
data-driven label transfer scheme for local glass boundary
estimation. A weighted voting scheme based on a joint feature
manifold is adopted to integrate depth and appearance cues, and
we learn a distance metric on the depth-encoded feature manifold.
In the third case, we make use of unlabeled data to relax the
annotation requirements for object detection and segmentation,
and propose a novel data-dependent margin distribution learning
criterion for boosting, which utilizes the intrinsic geometric
structure of datasets. One key aspect of this method is that it
can seamlessly incorporate unlabeled data by including a graph
Laplacian regularizer. We demonstrate the performance of our
models and compare with baseline methods on several real-world
object detection and segmentation tasks, including indoor object
detection, glass object segmentation and foreground segmentation
in video
- …