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A Mobile vision System for Robust Multi-Person Tracking

A Mobile Vision System for Robust Multi-Person Tracking

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

作者： Andreas Ess Bastian Leibe Konrad Schindler Luc Van Gool ETH Zurich Zurich Switzerland KU Leuven Leuven Belgium

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. the integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. the approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.

关键词： Machine vision Layout Feedback loop Object detection Mobile robots Noise robustness Cameras Data mining Detectors Video sequences

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Discriminative Learning of Visual Words for 3D Human Pose Estimation

Discriminative Learning of Visual Words for 3D Human Pose Es...

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

作者： Huazhong Ning Wei Xu Yihong Gong thomas Huang Department of ECE University of Illinois Urbana-Champaign USA NEC Laboratories of America Inc. USA

this paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. they capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. the learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an Appearance and Position Context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.

关键词： Humans Testing Clustering algorithms Videos pattern recognition Histograms Clustering methods Supervised learning National electric code Laboratories

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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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Reduce, Reuse & Recycle: Efficiently Solving Multi-Label MRFs

Reduce, Reuse & Recycle: Efficiently Solving Multi-Label MRF...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Karteek Alahari Pushmeet Kohli Philip H. S. Torr Oxford Brookes University UK Microsoft Research Limited Cambridge UK

In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. these methods are motivated by the observations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables;and (b) the number of primal variables involved in the energy function. Our first method (dynamic α-expansion) works by 'recycling' results from previous problem instances. the second method simplifies the energy function by 'reducing' the number of unknown variables, and can also be used to generate a good initialization for the dynamic α-expansion algorithm by 'reusing' dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching, and colour and object based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of α-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing, and max-product belief propagation. In most cases we achieve a 10-15 times speed-up in the computation time. Our modified α-expansion algorithm provides similar performance to Fast-PD [15]. However, it is much simpler and can be made orders of magnitude faster by using the initialization schemes proposed in the paper.

关键词： Recycling Minimization methods Belief propagation computer vision Message passing Stereo vision Videos Computational efficiency Inference algorithms Labeling

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Learning realistic human actions from movies

Learning realistic human actions from movies

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

作者： Ivan Laptev Marcin Marszalek Cordelia Schmid Benjamin Rozenfeld INRIA Rennes IRISA France INRIA Grenoble France LEAR-LJK Bar-llan University Israel

the aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. this challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. the method is shown to improve state-of-the-art results on the standard Kth action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.

关键词： Humans Motion pictures Image recognition Video sharing Layout Text categorization Object recognition Robustness Clothing Cameras

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Visual Tracking Via Incremental Log-Euclidean Riemannian Subspace Learning

Visual Tracking Via Incremental Log-Euclidean Riemannian Sub...

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

作者： Xi Li Weiming Hu Zhongfei Zhang Xiaoqin Zhang Mingliang Zhu Jian Cheng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China State University of New York Binghamton NY USA

Recently, a novel Log-Euclidean Riemannian metric [28] is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigen-basis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.

关键词： Covariance matrix Robustness Lighting Statistics Inference algorithms Particle tracking Particle filters Kernel Principal component analysis Cameras

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Dynamic scene shape reconstruction using a single structured light pattern

Dynamic scene shape reconstruction using a single structured...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Hiroshi Kawasaki Ryo Furukawa Ryusuke Sagawa Yasushi Yagi Saitama University Japan Faculty of Information Sciences Hiroshima City University Japan Institute of Scientific and Industrial Research Osaka University Japan

3D acquisition techniques to measure dynamic scenes and deformable objects with little texture are extensively researched for applications like the motion capturing of human facial expression. To allow such measurement, several techniques using structured light have been proposed. these techniques can be largely categorized into two types. the first involves techniques to temporally encode positional information of a projector's pixels using multiple projected patterns, and the second involves techniques to spatially encode positional information into areas or color spaces. Although the former allows dense reconstruction with a sufficient number of patterns, it has difficulty in scanning objects in rapid motion. the latter technique uses only a single pattern, so this problem can be resolved, however, it often uses complex patterns or color intensities, which are weak to noise, shape distortions, or textures. thus, it remains an open problem to achieve dense and stable 3D acquisition in real cases. In this paper, we propose a technique to achieve dense shape reconstruction that requires only a single-frame image of a grid pattern. the proposed technique also has the advantage of being robust in terms of image processing.

关键词： Layout Image reconstruction Colored noise Shape measurement Motion measurement Humans Spatial resolution Noise shaping Robustness Image processing

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Sketching in the Air: A vision-Based System for 3D Object Design

Sketching in the Air: A Vision-Based System for 3D Object De...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Yu Chen Jianzhuang Liu Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Hong Kong China Microsoft Research Asia Beijing China

3D object design has many applications including flexible 3D sketch input in CAD, computer game, webpage content design, image based object modeling, and 3D object retrieval. Most current 3D object design tools work on a 2D drawing plane such as computer screen or tablet, which is often inflexible with one dimension lost. On the other hand, virtual reality based methods have the drawbacks that there are awkward devices worn by the user and the virtual environment systems are expensive. In this paper, we propose a novel vision-based approach to 3D object design. Our system consists of a PC, a camera, and a mirror. We use the camera and mirror to track a wand so that the user can design 3D objects by sketching in 3D free space directly without having to wear any cumbersome devices. A number of new techniques are developed for working in this system, including input of object wireframes, gestures for editing and drawing objects, and optimization-based planar and curved surface generation. Our system provides designers a new user interface for designing 3D objects conveniently.

关键词： Cameras Mirrors Application software Design automation computer vision Image retrieval Content based retrieval Virtual reality Virtual environment User interfaces

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3D Model Matching with Viewpoint-Invariant Patches (VIP)

3D Model Matching with Viewpoint-Invariant Patches (VIP)

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

作者： Changchang Wu Brian Clipp Xiaowei Li Jan-Michael Frahm Marc Pollefeys Department of Computer Science North Carolina State University Chapel Hill NC USA Department of Computer Science ETH Zurich Switzerland

the robust alignment of images and scenes seen from widely different viewpoints is an important challenge for camera and scene reconstruction. this paper introduces a novel class of viewpoint independent local features for robust registration and novel algorithms to use the rich information of the new features for 3D scene alignment and large scale scene reconstruction. the key point of our approach consists of leveraging local shape information for the extraction of an invariant feature descriptor. the advantages of the novel viewpoint invariant patch (VIP) are: that the novel features are invariant to 3D camera motion and that a single VIP correspondence uniquely defines the 3D similarity transformation between two scenes. In the paper we demonstrate how to use the properties of the VIPs in an efficient matching scheme for 3D scene alignment. the algorithm is based on a hierarchical matching method which tests the components of the similarity transformation sequentially to allow efficient matching and 3D scene alignment. We evaluate the novel features on real data with known ground truth information and show that the features can be used to reconstruct large scale urban scenes.

关键词： Layout Image reconstruction Robustness Cameras Large-scale systems Geometry computer science Shape Data mining Sequential analysis

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