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Learning action maps of large environments via first-person vision

Learning action maps of large environments via first-person ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Rhinehart, Nicholas Kitani, Kris M. Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781467388511

When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-ofconcept application in which they are used in concert with activity detections to perform localization.

关键词： computer vision

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Rolling shutter camera relative pose: Generalized epipolar geometry

Rolling shutter camera relative pose: Generalized epipolar g...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Dai, Yuchao Li, Hongdong Kneip, Laurent Research School of Engineering Australian National University Australia ARC Centre of Excellence for Robotic Vision ACRV Australia

ISBN: (纸本)9781467388511

The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism. In dynamic geometric computer vision applications such as visual SLAM, the so-called rolling shutter effect therefore needs to be properly taken into account. A dedicated relative pose solver appears to be the first problem to solve, as it is of eminent importance to bootstrap any derivation of multi-view geometry. However, despite its significance, it has received inadequate attention to date. This paper presents a detailed investigation of the geometry of the rolling shutter relative pose problem. We introduce the rolling shutter essential matrix, and establish its link to existing models such as the push-broom cameras, summarized in a clean hierarchy of multi-perspective cameras. The generalization of well-established concepts from epipolar geometry is completed by a definition of the Sampson distance in the rolling shutter case. The work is concluded with a careful investigation of the introduced epipolar geometry for rolling shutter cameras on several dedicated benchmarks.

关键词： Geometry

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Detecting repeating objects using patch correlation analysis

Detecting repeating objects using patch correlation analysis

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Huberman, Inbar Fattal, Raanan School of Computer Science and Engineering Hebrew University of Jerusalem Israel

ISBN: (纸本)9781467388511

In this paper we describe a new method for detecting and counting a repeating object in an image. While the method relies on a fairly sophisticated deformable part model, unlike existing techniques it estimates the model parameters in an unsupervised fashion thus alleviating the need for a user-annotated training data and avoiding the associated specificity. This automatic fitting process is carried out by exploiting the recurrence of small image patches associated with the repeating object and analyzing their spatial correlation. The analysis allows us to reject outlier patches, recover the visual and shape parameters of the part model, and detect the object instances efficiently. In order to achieve a practical system which is able to cope with diverse images, we describe a simple and intuitive active-learning procedure that updates the object classification by querying the user on very few carefully chosen marginal classifications. Evaluation of the new method against the state-of-the-art techniques demonstrates its ability to achieve higher accuracy through a better user experience.

关键词： Object detection

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Recurrent attention models for depth-based person identification

Recurrent attention models for depth-based person identifica...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Haque, Albert Alahi, Alexandre Fei-Fei, Li Computer Science Department Stanford University United States

ISBN: (纸本)9781467388511

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.

关键词： Recurrent neural networks

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Constructing canonical regions for fast and effective view selection

Constructing canonical regions for fast and effective view s...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Wencheng Gao, Tianhao State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

In view selection, little work has been done for optimizing the search process; views must be densely distributed and checked individually. Thus, evaluating poor views wastes much time, and a poor view may even be misidentified as a best one. In this paper, we propose a search strategy by identifying the regions that are very likely to contain best views, referred to as canonical regions. It is by decomposing the model under investigation into meaningful parts, and using the canonical views of these parts to generate canonical regions. Applying existing view selection methods in the canonical regions can not only accelerate the search process but also guarantee the quality of obtained views. As a result, when our canonical regions are used for searching N-best views during comprehensive model analysis, we can attain greater search speed and reduce the number of views required. Experimental results show the effectiveness of our method.

关键词： computer vision

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A hierarchical pose-based approach to complex action understanding using dictionaries of actionlets and motion poselets

A hierarchical pose-based approach to complex action underst...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Lillo, Ivan Niebles, Juan Carlos Soto, Alvaro P. Universidad Catolica de Chile Santiago Chile Stanford University United States Universidad del Norte Colombia

ISBN: (纸本)9781467388511

In this paper, we introduce a new hierarchical model for human action recognition using body joint locations. Our model can categorize complex actions in videos, and perform spatio-temporal annotations of the atomic actions that compose the complex action being performed. That is, for each atomic action, the model generates temporal action annotations by estimating its starting and ending times, as well as, spatial annotations by inferring the human body parts that are involved in executing the action. Our model includes three key novel properties: (i) it can be trained with no spatial supervision, as it can automatically discover active body parts from temporal action annotations only; (ii) it jointly learns flexible representations for motion poselets and actionlets that encode the visual variability of body parts and atomic actions; (iii) a mechanism to discard idle or non-informative body parts which increases its robustness to common pose estimation errors. We evaluate the performance of our method using multiple action recognition benchmarks. Our model consistently outperforms baselines and state-of-the-art action recognition methods.

关键词： computer vision

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Information bottleneck learning using privileged information for visual recognition

Information bottleneck learning using privileged information...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Motiian, Saeid Piccirilli, Marco Adjeroh, Donald A. Doretto, Gianfranco West Virginia University MorgantownWV26508 United States

ISBN: (纸本)9781467388511

We explore the visual recognition problem from a main data view when an auxiliary data view is available during training. This is important because it allows improving the training of visual classifiers when paired additional data is cheaply available, and it improves the recognition from multi-view data when there is a missing view at testing time. The problem is challenging because of the intrinsic asymmetry caused by the missing auxiliary view during testing. We account for such view during training by extending the information bottleneck method, and by combining it with risk minimization. In this way, we establish an information theoretic principle for leaning any type of visual classifier under this particular setting. We use this principle to design a large-margin classifier with an efficient optimization in the primal space. We extensively compare our method with the state-of-the-art on different visual recognition datasets, and with different types of auxiliary data, and show that the proposed framework has a very promising potential.

关键词： Classification (of information)

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Inferring forces and learning human utilities from videos

Inferring forces and learning human utilities from videos

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhu, Yixin Jiang, Chenfanfu Zhao, Yibiao Terzopoulos, Demetri Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States UCLA Computer Graphics and Vision Laboratory United States

ISBN: (纸本)9781467388511

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordance in terms of the geometric compatibility between body poses and 3D objects, we devise algorithms that employ physicsbased simulation to infer the relevant forces/pressures acting on body parts. By observing the choices people make in videos (particularly in selecting a chair in which to sit) our system learns the comfort intervals of the forces exerted on body parts (while sitting). We account for people's preferences in terms of human utilities, which transcend comfort intervals to account also for meaningful tasks within scenes and spatiotemporal constraints in motion planning, such as for the purposes of robot task planning.

关键词： Robot programming

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Linear shape deformation models with local support using graph-based structured matrix factorisation

Linear shape deformation models with local support using gra...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Bernard, Florian Gemmar, Peter Hertel, Frank Goncalves, Jorge Thunberg, Johan Centre Hospitalier de Luxembourg Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg Trier University of Applied Sciences Trier Germany

ISBN: (纸本)9781467388511

Representing 3D shape deformations by highdimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation and sparse reconstruction, whereas for body shapes our method gives more realistic deformations.

关键词： Three dimensional computer graphics

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Learning structured inference neural networks with label relations

Learning structured inference neural networks with label rel...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Hu, Hexiang Zhou, Guang-Tong Deng, Zhiwei Liao, Zicheng Mori, Greg School of Computing Science Simon Fraser University BurnabyBC Canada College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China

ISBN: (纸本)9781467388511

Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with finegrained labels that describe major components, coarsegrained labels that depict high level abstraction, or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.

关键词： Image classification

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