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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是81-90 订阅

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L₀ Regularized Stationary Time Estimation for Crowd Group Analysis 27

<i>L</i><sub>0</sub> Regularized Stationary Time Estimation ...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yi, Shuai Wang, Xiaogang Lu, Cewu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L-0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. the proposed technologies are used to detect four types of stationary group activities and analyze crowd scene structures. We provide the first public benchmark dataset(1) for stationary time estimation and stationary group analysis.

关键词： pattern recognition

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Predicting Failures of vision Systems 27

Predicting Failures of Vision Systems

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Peng Wang, Jiuling Farhadi, Ali Hebert, Martial Parikh, Devi Virginia Tech Blacksburg VA 24061 USA Univ Texas Austin Austin TX 78712 USA Univ Washington Seattle WA 98195 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781479951178

computer vision systems today fail frequently. they also fail abruptly without warning or explanation. Alleviating the former has been the primary focus of the community. In this work, we hope to draw the community's attention to the latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems-semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets.

关键词： warning computer vision Systems computer vision failure prediction Classifiers

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Multi Label Generic Cuts: Optimal Inference in Multi Label Multi Clique MRF-MAP Problems 27

Multi Label Generic Cuts: Optimal Inference in Multi Label M...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Arora, Chetan Maheshwari, S. N. Hebrew Univ Jerusalem Jerusalem Israel Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781479951178

We propose an algorithm called Multi Label Generic Cuts (MLGC) for computing optimal solutions to MRF-MAP problems with submodular multi label multi-clique potentials. A transformation is introduced to convert a m-label k-clique problem to an equivalent 2-label (mk)-clique problem. We show that if the original multi-label problem is submodular then the transformed 2-label multi-clique problem is also submodular. We exploit sparseness in the feasible configurations of the transformed 2-label problem to suggest an improvement to Generic Cuts [3] to solve the 2-label problems efficiently. the algorithm runs in time O(m(k)n(3)) in the worst case (n is the number of pixels) generalizing O(2(k)n(3)) running time of Generic Cuts. We show experimentally that MLGC is an order of magnitude faster than the current state of the art [17, 20]. While the result of MLGC is optimal for submodular clique potential it is significantly better than the compared methods even for problems with non-submodular clique potential.

关键词： pattern recognition

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Ask the image: supervised pooling to preserve feature locality 27

Ask the image: supervised pooling to preserve feature locali...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Fanello, Sean Ryan Noceti, Nicoletta Ciliberto, Carlo Metta, Giorgio Odone, Francesca Ist Italiano Tecnol ICub Facil Milan Italy Univ Genoa DIBRIS Genoa Italy Microsoft Res Richmond VA USA

ISBN: (纸本)9781479951178

In this paper we propose a weighted supervised pooling method for visual recognition systems. We combine a standard Spatial Pyramid Representation which is commonly adopted to encode spatial information, with an appropriate Feature Space Representation favoring semantic information in an appropriate feature space. For the latter, we propose a weighted pooling strategy exploiting data supervision to weigh each local descriptor coherently with its likelihood to belong to a given object class. the two representations are then combined adaptively with Multiple Kernel Learning. Experiments on common benchmarks (Caltech-256 and PASCAL VOC-2007) show that our image representation improves the current visual recognition pipeline and it is competitive with similar state-of-art pooling methods. We also evaluate our method on a real Human-Robot Interaction setting, where the pure Spatial Pyramid Representation does not provide sufficient discriminative power, obtaining a remarkable improvement.

关键词： Human Robot Interaction Object Categorization Object recognition Supervised Pooling iCub iCubWorld Dataset

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A Compact and Discriminative Face Track Descriptor 27

A Compact and Discriminative Face Track Descriptor

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Parkhi, Omkar M. Simonyan, Karen Vedaldi, Andrea Zisserman, Andrew Univ Oxford Dept Engn Sci Visual Geometry Grp Oxford OX1 2JD England

ISBN: (纸本)9781479951178

Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification. To this end, we propose a novel face track descriptor, based on the Fisher Vector representation, and demonstrate that it has a number of favourable properties. First, the descriptor is suitable for tracks of both frontal and profile faces, and is insensitive to their pose. Second, the descriptor is compact due to discriminative dimensionality reduction, and it can be further compressed using binarization. third, the descriptor can be computed quickly (using hard quantization) and its compact size and fast computation render it very suitable for large scale visual repositories. Finally, the descriptor demonstrates good generalization when trained on one dataset and tested on another, reflecting its tolerance to the dataset bias. In the experiments we show that the descriptor exceeds the state of the art on both face verification task (YouTube Faces without outside training data, and INRIA-Buffy benchmarks), and face classification task (using the Oxford-Buffy dataset).

关键词： Binary Encoding Face recognition Face Verification Fisher Vectors Metric Learning YouTube Faces Dataset

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A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions 27

A Multigraph Representation for Improved Unsupervised/Semi-s...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Jones, Simon Shao, Ling Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781479951178

Graph-based methods are a useful class of methods for improving the performance of unsupervised and semi-supervised machine learning tasks, such as clustering or information retrieval. However, the performance of existing graph-based methods is highly dependent on how well the affinity graph reflects the original data structure. We propose that multimedia such as images or videos consist of multiple separate components, and therefore more than one graph is required to fully capture the relationship between them. Accordingly, we present a new spectral method - the Feature Grouped Spectral Multigraph (FGSM) - which comprises the following steps. First, mutually independent subsets of the original feature space are generated through feature clustering. Secondly, a separate graph is generated from each feature subset. Finally, a spectral embedding is calculated on each graph, and the embeddings are scaled/aggregated into a single representation. Using this representation, a variety of experiments are performed on three learning tasks clustering, retrieval and recognition - on human action datasets, demonstrating considerably better performance than the state-of-the-art.

关键词： Clustering Human Action recognition Manifold Learning Multimedia Retrieval Spectral Embedding Unsupervised Learning

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Complex Activity recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video 27

Complex Activity Recognition using Granger Constrained DBN (...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Swears, Eran Hoogs, Anthony Ji, Qiang Boyer, Kim Kitware Inc Clifton Pk NY 12065 USA Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA

ISBN: (纸本)9781479951178

Modeling interactions of multiple co-occurring objects in a complex activity is becoming increasingly popular in the video domain. the Dynamic Bayesian Network (DBN) has been applied to this problem in the past due to its natural ability to statistically capture complex temporal dependencies. However, standard DBN structure learning algorithms are generatively learned, require manual structure definitions, and/or are computationally complex or restrictive. We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner. this approach enables us to completely define the DBN structure prior to parameter learning, which reduces computational complexity in addition to providing a more descriptive structure. We refer to this modeling approach as the Granger Constraints DBN (GCDBN). Our experiments show how the GCDBN outperforms two of the most relevant state-of-the-art graphical models in complex activity classification on handball video data, surveillance data, and synthetic data.

关键词： activity recognition causality graphical models structure learning

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Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations 27

Full-Angle Quaternions for Robustly Matching Vectors of 3D R...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liwicki, Stephan Minh-Tri Pham Zafeiriou, Stefanos Pantic, Maja Stenger, Bjoern Univ London Imperial Coll Sci Technol & Med Comp Sci London SW7 2AZ England Toshiba Res Europe Ltd Cambridge Res Lab Cambridge England Univ Twente Elect Engn Math & Comp Sci NL-7500 AE Enschede Netherlands

ISBN: (纸本)9781479951178

In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale and translation which outperforms the previous state-of-the-art approach on a public dataset. For the latter, we incorporate online subspace learning with the proposed FAQ representation to highlight the benefits of the new representation.

关键词： Quaternion crop rotation Rotations FAQs (frequently asked questions) Euclidean shape recognition three Dimensions

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Unsupervised One-Class Learning for Automatic Outlier Removal 27

Unsupervised One-Class Learning for Automatic Outlier Remova...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Wei Hua, Gang Smith, John R. IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Stevens Inst Technol Hoboken NJ USA

ISBN: (纸本)9781479951178

Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source. Our approach works under a fully unsupervised manner, differing from traditional one-class learning supervised by known positive labels. By design, our approach optimizes a kernel-based max-margin objective which jointly learns a large margin one-class classifier and a soft label assignment for inliers and outliers. An alternating optimization algorithm is then designed to iteratively refine the classifier and the labeling, achieving a provably convergent solution in only a few iterations. Extensive experiments conducted on four image datasets in the presence of artificial and real-world outliers demonstrate that the proposed approach is considerably superior to the state-of-the-arts in obliterating outliers from contaminated one class of images, exhibiting strong robustness at a high outlier proportion up to 60%.

关键词： One-Class Learning Outlier Removal outliers images computer vision inliers pattern recognition problem Classifiers Data Collection Methods Data sources AUTOMATIC Optimization algorithms

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Human Shape and Pose Tracking Using Keyframes 27

Human Shape and Pose Tracking Using Keyframes

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Huang, Chun-Hao Boyer, Edmond Navab, Nassir Ilic, Slobodan Tech Univ Munich D-80290 Munich Germany LJK INRIA Grenoble Rhone Alpes Grenoble France

ISBN: (纸本)9781479951178

this paper considers human tracking in multi-view setups and investigates a robust strategy that learns online key poses to drive a shape tracking method. the interest arises in realistic dynamic scenes where occlusions or segmentation errors occur. the corrupted observations present missing data and outliers that deteriorate tracking results. We propose to use key poses of the tracked person as multiple reference models. In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses. they provide therefore better initial hypotheses when tracking with noisy data. Our approach identifies these reference models online as distinctive keyframes during tracking. the most suitable one is then chosen as the reference at each frame. In addition, taking advantage of the proximity between successive frames, an efficient outlier handling technique is proposed to prevent from associating the model to irrelevant outliers. the two strategies are successfully experimented with a surface deformation framework that recovers both the pose and the shape. Evaluations on existing datasets also demonstrate their benefits with respect to the state of the art.

关键词： Gesture recognition

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