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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141-150 订阅

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Gesture recognition in Ego-Centric Videos using Dense Trajectories and Hand Segmentation

Gesture Recognition in Ego-Centric Videos using Dense Trajec...

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

作者： Baraldi, Lorenzo Paci, Francesco Serra, Giuseppe Benini, Luca Cucchiara, Rita Univ Modena & Reggio Emilia Dipartimento Ingn Enzo Ferrari Modena MO Italy Univ Bologna DEI Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781479943098

We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively test our gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.

关键词： Gesture recognition

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Describing Textures in the Wild 27

Describing Textures in the Wild

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

作者： Cimpoi, Mircea Maji, Subhransu Kokkinos, Iasonas Mohamed, Sammy Vedaldi, Andrea Univ Oxford Oxford OX1 2JD England Toyota Technol Inst Nagoya Aichi Japan Ecole Cent Paris INRIA Saclay Paris France

ISBN: (纸本)9781479951178

patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". the resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks;in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and Kth-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

关键词： Fisher Vector attribute convolutional neural network material recognition texture

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Human Action recognition across Datasets by Foreground-weighted Histogram Decomposition 27

Human Action Recognition across Datasets by Foreground-weigh...

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

作者： Sultani, Waqas Saleemi, Imran Univ Cent Florida Ctr Comp Vis Res Orlando FL 32816 USA

ISBN: (纸本)9781479951178

this paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training. In this scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. We first explore reasons for poor classifier performance when tested on novel datasets, and quantify the effect of scene backgrounds on action representations and recognition. Using only the background features and partitioning of gist feature space, we show that the background scenes in recent datasets are quite discriminative and can be used classify an action with reasonable accuracy. We then propose a new process to obtain a measure of confidence in each pixel of the video being a foreground region, using motion, appearance, and saliency together in a 3D MRF based framework. We also propose multiple ways to exploit the foreground confidence: to improve bag-of-words vocabulary, histogram representation of a video, and a novel histogram decomposition based representation and kernel. We used these foreground confidences to recognize actions trained on one data set and test on a different data set. We have performed extensive experiments on several datasets that improve cross dataset recognition accuracy as compared to baseline methods.

关键词： action recognition cross dataset dataset bias foreground weighted representation

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Gait recognition under Speed Transition 27

Gait Recognition under Speed Transition

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

作者： Mansur, Al Makihara, Yasushi Aqmar, Rasyid Yagi, Yasushi Osaka Univ Inst Sci & Ind Res Ibaraki Osaka 5670047 Japan

ISBN: (纸本)9781479951178

this paper describes a method of gait recognition from image sequences wherein a subject is accelerating or decelerating. As a speed change occurs due to a change of pitch (the first-order derivative of a phase, namely, a gait stance) and/or stride, we model this speed change using a cylindrical manifold whose azimuth and height corresponds to the phase and the stride, respectively. A radial basis function (RBF) interpolation framework is used to learn subject specific mapping matrices for mapping from manifold to image space. Given an input image sequence of speed transited gait of a test subject, we estimate the mapping matrix of the test subject as well as the phase and stride sequence using an energy minimization framework considering the following three points: (1) fitness of the synthesized images to the input image sequence as well as to an eigenspace constructed by exemplars of training subjects;(2) smoothness of the phase and the stride sequence;and (3) pitch and stride fitness to the pitch-stride preference model. Using the estimated mapping matrix, we synthesize a constant-speed gait image sequence, and extract a conventional period-based gait feature from it for matching. We conducted experiments using real speed transited gait image sequences with 179 subjects and demonstrated the effectiveness of the proposed method.

关键词： image sequences Variable speed Locomotion Gait recognition Gait Phase MAPPING(MAthEMATICAL) Manifolds azimuth ENERGY MINIMISATION

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vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Vision on Wheels: Looking at Driver, Vehicle, and Surround f...

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

作者： Ohn-Bar, Eshed Tawari, Ashish Martin, Sujitha Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab San Diego CA 92103 USA

ISBN: (纸本)9781479943098

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. the implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.

关键词： active safety driver assistance systems mobile vision applications real-time behavior analysis temporal action recognition

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From Ego to Nos-vision: Detecting Social Relationships in First-Person Views

From Ego to Nos-vision: Detecting Social Relationships in Fi...

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

作者： Alletto, Stefano Serra, Giuseppe Calderara, Simone Solera, Francesco Cucchiara, Rita Univ Modena & Reggio Emilia I-41125 Modena Italy

ISBN: (纸本)9781479943098

In this paper we present a novel approach to detect groups in ego-vision scenarios. People in the scene are tracked through the video sequence and their head pose and 3D location are estimated. Based on the concept of f-formation, we define with the orientation and distance an inherently social pairwise feature that describes the affinity of a pair of people in the scene. We apply a correlation clustering algorithm that merges pairs of people into socially related groups. Due to the very shifting nature of social interactions and the different meanings that orientations and distances can assume in different contexts, we learn the weight vector of the correlation clustering using Structural SVMs. We extensively test our approach on two publicly available datasets showing encouraging results when detecting groups from first-person camera views.

关键词： Ego-vision f-formation social interaction

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Superpixel Estimation for Hyperspectral Imagery

Superpixel Estimation for Hyperspectral Imagery

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

作者： Massoudifar, Pegah Rangarajan, Anand Gader, Paul Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781479943098

In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation has emerged in this area. Here, we present an approach to superpixel estimation in hyperspectral images by adapting the well known UCM approach to hyperspectral volumes. this approach benefits from the channel information at each pixel of the hyperspectral image while obtaining a compact representation of the hyperspectral volume using principal component analysis. Our experimental evaluation demonstrates that the additional information of spectral channels will substantially improve superpixel estimation from a single "monochromatic" channel. Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.

关键词： hyperspectral imagery information channels image segmentation Compact Compact pattern pixel extra message principal components analysis wavelengths

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Clothing Co-Parsing by Joint Image Segmentation and Labeling 27

Clothing Co-Parsing by Joint Image Segmentation and Labeling

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

作者： Yang, Wei Luo, Ping Lin, Liang Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China SYSU CMU Shunde Int Joint Res Inst Shunde Peoples R China

ISBN: (纸本)9781479951178

this paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. the first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). the joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.

关键词： Clothing recognition EM Algorithm Human Parsing Image Understand

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An Attention-based Activity recognition for Egocentric Video

An Attention-based Activity Recognition for Egocentric Video

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

作者： Matsuo, Kenji Yamada, Kentaro Ueno, Satoshi Naito, Sei KDDI R&D Labs Inc Fujimino Saitama Japan

ISBN: (纸本)9781479943098

In this paper, we propose a human activity recognition method from first-person videos, which provides a supplementary method to improve the recognition accuracy. Conventional methods detect objects and derive a user's behavior based on their taxonomy. One of the recent works has achieved accuracy improvement by determining key objects based on hand manipulation. However, such manipulation-based approach has a restriction on applicable scenes and object types because the user's hands don't always present significant information. In contrast, our proposed attention-based approach provides a solution to detect visually salient objects as key objects in a non-contact manner. Experimental results show that the proposed method classifies first-person actions more accurately than the previous method by 6.4 percentage points and its average accuracy reaches 43.3%.

关键词： Object detection

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Efficient Boosted Exemplar-based Face Detection 27

Efficient Boosted Exemplar-based Face Detection

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

作者： Li, Haoxiang Lin, Zhe Brandt, Jonathan Shen, Xiaohui Hua, Gang Stevens Inst Technol Hoboken NJ 07030 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9781479951178

Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved. Challenging conditions, such as extreme pose, lighting, and occlusion, have historically hampered traditional, model-based methods. In contrast, exemplar-based face detection has been shown to be effective, even under these challenging conditions, primarily because a large exemplar database is leveraged to cover all possible visual variations. However, relying heavily on a large exemplar database to deal with the face appearance variations makes the detector impractical due to the high space and time complexity. We construct an efficient boosted exemplar-based face detector which overcomes the defect of the previous work by being faster, more memory efficient, and more accurate. In our method, exemplars as weak detectors are discriminatively trained and selectively assembled in the boosting framework which largely reduces the number of required exemplars. Notably, we propose to include non-face images as negative exemplars to actively suppress false detections to further improve the detection accuracy. We verify our approach over two public face detection benchmarks and one personal photo album, and achieve significant improvement over the state-of-the-art algorithms in terms of both accuracy and efficiency.

关键词： Face recognition

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