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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是981-990 订阅

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Fusing Spatiotemporal Features and Joints for 3D Action recognition

Fusing Spatiotemporal Features and Joints for 3D Action Reco...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Yu Chen, Wenbin Guo, Guodong W Virginia Univ Dept CSEE Morgantown WV 26506 USA

ISBN: (纸本)9780769549903

We present a novel approach to 3D human action recognition based on a feature-level fusion of spatiotemporal features and skeleton joints. First, 3D interest points detection and local feature description are performed to extract spatiotemporal motion information. then the frame difference and pairwise distances of skeleton joint positions are computed to characterize the spatial information of the joints in 3D space. these two features are complementary to each other. A fusion scheme is then proposed to combine them effectively based on the random forests method. the proposed approach is validated on three challenging 3D action datasets for human action recognition. Experimental results show that the proposed approach outperforms the state-of-the-art methods on all three datasets.

关键词： feature extraction gesture recognition image fusion image motion analysis image thinning stereo image processing trees (mathematics)

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Emotional Expression Classification using Time-Series Kernels

Emotional Expression Classification using Time-Series Kernel...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lorincz, Andras Jeni, Laszlo A. Szabo, Zoltan Cohn, Jeffrey F. Kanade, Takeo Eotvos Lorand Univ Budapest Hungary Carnegie Mellon Univ Pittsburgh PA 15106 USA Univ Pittsburgh Pittsburgh PA 15106 USA

ISBN: (纸本)9780769549903

Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures. We achieved over 99% accuracy - measured by area under ROC curve - using only the 'motion pattern' of the PCA compressed representation of the marker point vector, the so-called shape parameters. Beyond the classification of full motion patterns, several expressions were recognized with over 90% accuracy in as few as 5-6 frames from their onset, about 200 milliseconds.

关键词： emotion recognition face recognition image motion analysis principal component analysis support vector machines time series time warp simulation

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Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces

Improved Image Set Classification via Joint Sparse Approxima...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Shaokang Sanderson, Conrad Harandi, Mehrtash T. Lovell, Brian C. Univ Queensland Sch ITEE Brisbane Qld 4072 Australia

ISBN: (纸本)9780769549897

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, Eth-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

关键词： approximation theory feature extraction image classification image matching image reconstruction image representation pattern clustering pose estimation

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Evaluating new variants of Motion Interchange patterns

Evaluating new variants of Motion Interchange Patterns

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hanani, Yair Levy, Noga Wolf, Lior Tel Aviv Univ IL-69978 Tel Aviv Israel

ISBN: (纸本)9780769549903

Action recognition in videos is an active research field that is fueled by an acute need, spanning several application domains. Still, existing systems fall short of the applications' needs in real-world scenarios, where the quality of the video is less than optimal and the viewpoint is uncontrolled and often not static. In this paper, we extend the Motion Interchange patterns (MIP) framework for action recognition. this effective framework encodes motion by capturing local changes in motion directions and additionally uses mechanisms to suppress static edges and compensate for global camera motion. Here, we suggest to apply the MIP encoding on gradient-based descriptors to enhance invariance to light changes and achieve a better description of the motion's structure. We compare our method using patterns of Oriented Edge Magnitudes (POEM) and Difference of Gaussians (DoG) as gradient-based descriptors to the original MIP on two challenging large-scale datasets.

关键词： image coding image motion analysis image recognition

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Motionlets: Mid-Level 3D Parts for Human Motion recognition

Motionlets: Mid-Level 3D Parts for Human Motion Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, LiMin Qiao, Yu Tang, Xiaoou Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

this paper proposes motionlet, a mid-level and spatiotemporal part, for human motion recognition. Motionlet can be seen as a tight cluster in motion and appearance space, corresponding to the moving process of different body parts. We postulate three key properties of motionlet for action recognition: high motion saliency, multiple scale representation, and representative-discriminative ability. Towards this goal, we develop a data-driven approach to learn motionlets from training videos. First, we extract 3D regions with high motion saliency. then we cluster these regions and preserve the centers as candidate templates for motionlet. Finally, we examine the representative and discriminative power of the candidates, and introduce a greedy method to select effective candidates. With motionlets, we present a mid-level representation for video, called motionlet activation vector. We conduct experiments on three datasets, Kth, HMDB51, and UCF50. the results show that the proposed methods significantly outperform state-of-the-art methods.

关键词： computer graphics feature extraction image motion analysis video signal processing

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Event recognition in Videos by Learning from Heterogeneous Web Sources

Event Recognition in Videos by Learning from Heterogeneous W...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Lin Duan, Lixin Xu, Dong Nanyang Technol Univ Sch Comp Engn Singapore Singapore SAP Res Singapore Singapore

ISBN: (纸本)9780769549897

In this work, we propose to leverage a large number of loosely labeled web videos (e. g., from YouTube) and web images (e. g., from Google/Bing image search) for visual event recognition in consumer videos without requiring any labeled consumer videos. We formulate this task as a new multi-domain adaptation problem with heterogeneous sources, in which the samples from different source domains can be represented by different types of features with different dimensions (e. g., the SIFT features from web images and space-time (ST) features from web videos) while the target domain samples have all types of features. To effectively cope with the heterogeneous sources where some source domains are more relevant to the target domain, we propose a new method called Multi-domain Adaptation with Heterogeneous Sources (MDA-HS) to learn an optimal target classifier, in which we simultaneously seek the optimal weights for different source domains with different types of features as well as infer the labels of unlabeled target domain data based on multiple types of features. We solve our optimization problem by using the cutting-plane algorithm based on group-based multiple kernel learning. Comprehensive experiments on two datasets demonstrate the effectiveness of MDA-HS for event recognition in consumer videos.

关键词： Internet image classification image recognition learning (artificial intelligence) optimisation video signal processing

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Grassmannian Sparse Representations and Motion Depth Surfaces for 3D Action recognition

Grassmannian Sparse Representations and Motion Depth Surface...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Azary, Sherif Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780769549903

Manifold learning has been effectively used in computer vision applications for dimensionality reduction that improves classification performance and reduces computational load. Grassmann manifolds are well suited for computer vision problems because they promote smooth surfaces where points are represented as subspaces. In this paper we propose Grassmannian Sparse Representations (GSR), a novel subspace learning algorithm that combines the benefits of Grassmann manifolds with sparse representations using least squares loss L1-norm minimization for optimal classification. We further introduce a new descriptor that we term Motion Depth Surface (MDS) and compare its classification performance against the traditional Motion History Image (MHI) descriptor. We demonstrate the effectiveness of GSR on computationally intensive 3D action sequences from the Microsoft Research 3D-Action and 3D-Gesture datasets.

关键词： computer vision image classification image motion analysis image representation least squares approximations minimisation

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Robust Multi-Resolution Pedestrian Detection in Traffic Scenes

Robust Multi-Resolution Pedestrian Detection in Traffic Scen...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yan, Junjie Zhang, Xucong Lei, Zhen Liao, Shengcai Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Ctr Biometr & Secur Res Natl Lab Pattern Recognition Beijing Peoples R China

ISBN: (纸本)9780769549897

the serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. the model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. the context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).

关键词： image resolution object detection pedestrians traffic engineering computing

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Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences

Robust Canonical Time Warping for the Alignment of Grossly C...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Panagakis, Yannis Nicolaou, Mihalis A. Zafeiriou, Stefanos Pantic, Maja Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9780769549897

Temporal alignment of human behaviour from visual data is a very challenging problem due to a numerous reasons, including possible large temporal scale differences, inter/intra subject variability and, more importantly, due to the presence of gross errors and outliers. Gross errors are often in abundance due to incorrect localization and tracking, presence of partial occlusion etc. Furthermore, such errors rarely follow a Gaussian distribution, which is the de-facto assumption in machine learning methods. In this paper, building on recent advances on rank minimization and compressive sensing, a novel, robust to gross errors temporal alignment method is proposed. While previous approaches combine the dynamic time warping (DTW) with low-dimensional projections that maximally correlate two sequences, we aim to learn two underlying projection matrices ( one for each sequence), which not only maximally correlate the sequences but, at the same time, efficiently remove the possible corruptions in any datum in the sequences. the projections are obtained by minimizing the weighted sum of nuclear and l(1) norms, by solving a sequence of convex optimization problems, while the temporal alignment is found by applying the DTW in an alternating fashion. the superiority of the proposed method against the state-of-the-art time alignment methods, namely the canonical time warping and the generalized time warping, is indicated by the experimental results on both synthetic and real datasets.

关键词： Gaussian distribution computer vision convex programming data visualisation image sequences learning (artificial intelligence) matrix algebra minimisation

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Stereo vision Algorithms for FPGAs

Stereo Vision Algorithms for FPGAs

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mattoccia, Stefano Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy

ISBN: (纸本)9780769549903

In recent years, with the advent of cheap and accurate RGBD (RGB plus Depth) active sensors like the Microsoft Kinect and devices based on time-of-flight (ToF) technology, there has been increasing interest in 3D-based applications. At the same time, several effective improvements to passive stereo vision algorithms have been proposed in the literature. Despite these facts and the frequent deployment of stereo vision for many research activities, it is often perceived as a bulky and expensive technology not well suited to consumer applications. In this paper, we will review a subset of state-of-the-art stereo vision algorithms that have the potential to fit a target computing architecture based on low-cost field-programmable gate arrays (FPGAs), without additional external devices (e. g., FIFOs, DDR memories, etc.). Mapping these algorithms into a similar low-power, low-cost architecture would make RGBD sensors based on stereo vision suitable to a wider class of application scenarios currently not addressed by this technology.

关键词： computer vision field programmable gate arrays image sensors stereo image processing

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