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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是551-560 订阅

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Learning Human Actions via Information Maximization

Learning Human Actions via Information Maximization

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Jingen Liu Mubarak Shah Computer Vision Laboratory University of Central Florida USA

In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a bag of spatiotemporal features called video-words by quantizing the extracted 3D interest points (cuboids) from the videos. Our proposed approach is able to automatically discover the optimal number of video-word clusters by utilizing Maximization of Mutual Information (MMI). Unlike the k-means algorithm, which is typically used to cluster spatiotemporal cuboids into video words based on their appearance similarity, MMI clustering further groups the video-words, which are highly correlated to some group of actions. To capture the structural information of the learnt optimal video-word clusters, we explore the correlation of the compact video-word clusters. We use the modified correlgoram, which is not only translation and rotation invariant, but also somewhat scale invariant. We extensively test our proposed approach on two publicly available challenging datasets: the Kth dataset and IXMAS multiview dataset. To the best of our knowledge, we are the first to try the bag of video-words related approach on the multiview dataset. We have obtained very impressive results on both datasets.

关键词： Humans Spatiotemporal phenomena Data mining computer vision Video sequences Image motion analysis Feature extraction Cameras Clustering algorithms Testing

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Action MACH: A Spatio-temporal Maximum Average Correlation Height Filter for Action recognition

Action MACH: A Spatio-temporal Maximum Average Correlation H...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Mikel D. Rodriguez Javed Ahmed Mubarak Shah Computer Vision Laboratory University of Central Florida Orlando FL USA Military College of Signals Rawalpindi Pakistan

In this paper we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.

关键词： Humans Image motion analysis Data analysis Fourier transforms Optical films Optical filters computer vision Spatiotemporal phenomena Frequency domain analysis Computational efficiency

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Sequential Particle Swarm Optimization for Visual Tracking

Sequential Particle Swarm Optimization for Visual Tracking

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Xiaoqin Zhang Weiming Hu Steve Maybank Xi Li Mingliang Zhu National Laboratory of Pattern Recognition Institute of Automation Beijing China School of Computer Science and Information Systems Birkbeck College London UK

Visual tracking usually involves an optimization process for estimating the motion of an object from measured images in a video sequence. In this paper, a new evolutionary approach, PSO (particle swarm optimization), is adopted for visual tracking. Since the tracking process is a dynamic optimization problem which is simultaneously influenced by the object state and the time, we propose a sequential particle swarm optimization framework by incorporating the temporal continuity information into the traditional PSO algorithm. In addition, the parameters in PSO are changed adaptively according to the fitness values of particles and the predicted motion of the tracked object, leading to a favourable performance in tracking applications. Furthermore, we show theoretically that, in a Bayesian inference view, the sequential PSO framework is in essence a multi-layer importance sampling based particle filter. Experimental results demonstrate that, compared with the state-of-the-art particle filter and its variation-the unscented particle filter, the proposed tracking algorithm is more robust and effective, especially when the object has an arbitrary motion or undergoes large appearance changes.

关键词： Particle swarm optimization Particle tracking Particle filters Inference algorithms Motion estimation Motion measurement Video sequences Bayesian methods Nonhomogeneous media Monte Carlo methods

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FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation

FusionFlow: Discrete-Continuous Optimization for Optical Flo...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Victor Lempitsky Stefan Roth Carsten Rother Microsoft Research Cambridge UK Technical University of Darmstadt Germany

Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. the modeling accuracy of the energy in this case is often traded for its tractability. this is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. the obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art.

关键词： Image motion analysis Optical saturation Statistics Optimization methods State estimation Stereo vision Iterative algorithms Fuses Image sequences computer vision

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Annotating Collections of Photos Using Hierarchical Event and Scene Models

Annotating Collections of Photos Using Hierarchical Event an...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Liangliang Cao Jiebo Luo Henry Kautz thomas S. Huang Department of Electrical and Computer Engineering University of Illinois USA Kodak Research Laboratories Eastman Kodak (Japan) Limited USA

Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and explore the associated GPS and time information for semantic annotation. First, we employ a constrained clustering method to partition a photo collection into event-based sub-collections, considering that the GPS records may be partly missing (a practical issue). We then use conditional random field (CRF) models to exploit the correlation between photos based on (1) time-location constraints and (2) the relationship between collection-level annotation (i.e., events) and image-level annotation (i.e., scenes). With the introduction of such a multi-level annotation hierarchy, our system addresses the problem of annotating consumer photo collections that requires a more hierarchical description of the customers' activities than do the simpler image annotation tasks. the efficacy of the proposed system is validated using a geotagged customer photo collection database, which consists of over 100 folders and is labeled for 12 events and 12 scenes.

关键词： Layout Global Positioning System computer vision Information resources Cameras Laboratories Clustering methods Image databases Image retrieval Event detection

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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Inside-Outside Net: Detecting Objects in Context with Skip P...

引用

ieee conference on computer vision and pattern recognition

作者： Sean Bell C. Lawrence Zitnick Kavita Bala Ross Girshick Cornell University Microsoft Research

ISBN: (纸本)9781467388528

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 77.9% mAP. On the new and more challenging MS COCO dataset, we improve state-of-the-art from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won "Best Student Entry" and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.

关键词： Context Ions Recurrent neural networks Feature extraction Convolution computer architecture Detectors

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Efficient Mean Shift Belief Propagation for vision Tracking

Efficient Mean Shift Belief Propagation for Vision Tracking

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Minwoo Park Yanxi Liu Robert T. Collins Department of Computer Science and Engineering Pennsylvania State University University Park PA USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

A mechanism for efficient mean-shift belief propagation (MSBP) is introduced. the novelty of our work is to use mean-shift to perform nonparametric mode-seeking on belief surfaces generated within the belief propagation framework. Belief Propagation (BP) is a powerful solution for performing inference in graphical models. However, there is a quadratic increase in the cost of computation with respect to the size of the hidden variable space. While the recently proposed nonparametric belief propagation (NBP) has better performance in terms of speed, even for continuous hidden variable spaces, computation is still slow due to the particle filter sampling process. Our MSBP method only needs to compute a local grid of samples of the belief surface during each iteration. this approach needs a significantly smaller number of samples than NBP, reducing computation time, yet it also yields more accurate and stable solutions. the efficiency and robustness of MSBP is compared against other variants of BP on applications in multi-target tracking and 2D articulated body tracking.

关键词： Belief propagation Graphical models Robustness Spatial resolution Lattices computer science Computational efficiency Particle filters Sampling methods Grid computing

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Classifiability-Based Optimal Discriminatory Projection Pursuit

Classifiability-Based Optimal Discriminatory Projection Purs...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Yu Su Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Digital Media Research Center Institute of Computing Technology Chinese Academy and Sciences China Digital Media Research Center Institute of Computing Technology CAS China Institute for Digital Media Peking University China

Linear Discriminant Analysis (LDA) might be the most widely used linear feature extraction method in pattern recognition. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new computational paradigm named Optimal Discriminatory Projection Pursuit (ODPP), which is totally different from the traditional LDA and its variants. Only two simple steps are involved in the proposed ODPP: one is the construction of candidate projection set;the other is the optimal discriminatory projection pursuit. For the former step, candidate projections are generated as the difference vectors between nearest between-class boundary samples with redundancy well-controlled, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the large candidate projection set. We show that the new "projection pursuit" paradigm not only does not suffer from the limitations of the traditional LDA but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experimental comparisons with LDA and its variants on synthetic and real data sets show that the proposed method consistently has better performances.

关键词： Linear discriminant analysis Scattering Feature extraction Gaussian distribution Null space computer science Content addressable storage Laboratories Information processing pattern recognition

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Selective Hidden Random Fields: Exploiting Domain-Specific Saliency for Event Classification

Selective Hidden Random Fields: Exploiting Domain-Specific S...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Vidit Jain Amit Singhal Jiebo Luo University of Massachusetts Amherst Amherst MA USA Eastman Kodak (Japan) Limited Rochester NY USA

Classifying an event captured in an image is useful for understanding the contents of the image. the captured event provides context to refine models for the presence and appearance of various entities, such as people and objects, in the captured scene. Such contextual processing facilitates the generation of better abstractions and annotations for the image. Consider a typical set of consumer images with sports-related content. these images are taken mostly by amateur photographers, and often at a distance. In the absence of manual annotation or other sources of information such as time and location, typical recognition tasks are formidable on these images. Identifying the sporting event in these images provides a context for further recognition and annotation tasks. We propose to use the domain-specific saliency of the appearances of the playing surfaces, and ignore the noninformative parts of the image such as crowd regions, to discriminate among different sports. To this end, we present a variation of the hidden-state conditional random field that selects a subset of the observed features suitable for classification. the inferred hidden variables in this model represent a selection criteria desirable for the problem domain. For sports-related images, this selection criteria corresponds to the segmentation of the playing surface in the image. We demonstrate the utility of this model on consumer images collected from the Internet.

关键词： Image recognition Image segmentation Layout Information resources Image resolution Context modeling Internet Digital photography Explosions Image retrieval

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Distributed Data Association and Filtering for Multiple Target Tracking

Distributed Data Association and Filtering for Multiple Targ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Ting Yu Ying Wu Nils O. Krahnstoever Peter H. Tu GE Global Research Visualization and Computer Vision Laboratory Niskayuna NY USA Department of Electrical Engineering and Computer Sciences Northwestern University Evanston IL USA

this paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackers' motion and association variables is adopted. Considering the interleaved nature of data association and tracker filtering, the multi-target tracking is formulated as a missing data problem, and the solution is found by the proposed variational EM algorithm. We analytically show that 1) the posteriori distributions of trackers' motions (the real interests in terms of tracking applications) can be effectively computed in the E-step of the EM iterations, and 2) the solution of trackers' association variables can be pursued under a derived graph-based discrete optimization formulation, thus efficiently estimated in the M-step by the recently emerging graph optimization algorithms. the proposed approach is very general such that sophisticated data association priori and likelihood function can be easily incorporated. this general framework is tested with both simulation data and real world surveillance video. the reported qualitative and quantitative studies verify the effectiveness and low computational cost of the algorithm.

关键词： Target tracking Distributed computing Filtering algorithms Algorithm design and analysis Motion analysis Motion estimation Testing Computational modeling Surveillance Computational efficiency

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