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

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Ground Truth For Pedestrian Analysis and Application to Camera Calibration

Ground Truth For Pedestrian Analysis and Application to Came...

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

作者： Creusot, Clement Courty, Nicolas Toshiba R&D Ctr Kawasaki Kanagawa Japan UBS IRISA Vannes France

ISBN: (纸本)9780769549903

this paper investigates the use of synthetic 3D scenes to generate ground truth of pedestrian segmentation in 2D crowd video data. Manual segmentation of objects in videos is indeed one of the most time-consuming type of assisted labeling. A big gap in computer vision research can not be filled due to this lack of temporally dense and precise segmentation ground truth on large video samples. Such data is indeed essential to introduce machine learning techniques for automatic pedestrian segmentation, as well as many other applications involving occluded people. We present a new dataset of 1.8 million pedestrian silhouettes presenting human-to-human occlusion patterns likely to be seen in real crowd video data. To our knowledge, it is the first publicly available large dataset of pedestrian in crowd silhouettes. Solutions to generate and represent this data are detailed. We discuss ideas of how this ground truth can be used for a large number of computer vision applications and demonstrate it on a camera calibration toy problem.

关键词： calibration cameras computer vision image segmentation learning (artificial intelligence) video signal processing

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Face recognition Across Poses Using A Single 3D Reference Model

Face Recognition Across Poses Using A Single 3D Reference Mo...

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

作者： Hsu, Gee-Sern Peng, Hsiao-Chia Natl Taiwan Univ Sci & Technol Taipei 106 Taiwan

ISBN: (纸本)9780769549903

Approaches for cross-pose face recognition can be split into 2D image based and 3D model based. Many 2D based methods are reported with promising performance but can only work for poses same as those in the training set. Although 3D based methods can handle arbitrary poses, only a small number of approaches are available. Extended from a latest face reconstruction method using a single 3D reference model, this study focuses on using the reconstructed 3D face for recognition. the reconstructed 3D face allows the generation of multi-pose samples for recognition. the recognition performance varies with poses, the closer the pose to the frontal, the better the performance attained. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. these attempts make this approach competitive to the state-of-the-art methods.

关键词： face recognition image reconstruction pose estimation solid modelling

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Detecting and Aligning Faces by Image Retrieval

Detecting and Aligning Faces by Image Retrieval

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

作者： Shen, Xiaohui Lin, Zhe Brandt, Jonathan Wu, Ying Northwestern Univ 2145 Sheridan Rd Evanston IL 60208 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9780769549897

Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. the same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.

关键词： face recognition image retrieval learning (artificial intelligence) object detection

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Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object recognition and Detection

Incorporating Structural Alternatives and Sharing into Hiera...

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

作者： Wang, Xiaolong Lin, Liang Huang, Lichao Yan, Shuicheng Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Natl Univ Singapore Dept ECE Singapore Singapore

ISBN: (纸本)9780769549897

this paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance. Compared with well acknowledged hierarchical models, we study two advanced capabilities in hierarchy for object modeling: (i)"switch" variables(i.e. or-nodes) for specifying alternative compositions, and (ii) making local classifiers (i. e. leaf-nodes) shared among different classes. these capabilities enable us to account well for structural variabilities while preserving the model compact. Our model, in the form of an And-Or Graph, comprises four layers: a batch of leaf-nodes with collaborative edges in bottom for localizing object parts;the or-nodes over bottom to activate their children leaf-nodes;the and-nodes to classify objects as a whole;one root-node on the top for switching multiclass classification, which is also an or-node. For model training, we present an EM-type algorithm, namely dynamical structural optimization (DSO), to iteratively determine the structural configuration, (e.g., leaf-node generation associated with their parent or-nodes and shared across other classes), along with optimizing multi-layer parameters. the proposed method is valid on challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves state-of-the-arts performance.

关键词： graph theory image classification object detection object recognition optimisation

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thETIS: three Dimensional Tennis Shots A human action dataset

THETIS: THree Dimensional Tennis Shots A human action datase...

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

作者： Gourgari, Sofia Goudelis, Georgios Karpouzis, Konstantinos Kollias, Stefanos Natl Tech Univ Athens Image Video & Multimedia Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9780769549903

the detection and classification of human movements, as a joint field of computer vision and pattern recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. the database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. the database is freely available for research purposes.

关键词： computer vision feature extraction image classification image motion analysis interactive devices object detection object recognition sport video databases

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A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

A Comparative Study of Modern Inference Techniques for Discr...

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

作者： Kappes, Joerg H. Andres, Bjoern Hamprecht, Fred A. Schnoerr, Christoph Nowozin, Sebastian Batra, Dhruv Kim, Sungwoong Kausler, Bernhard X. Lellmann, Jan Komodakis, Nikos Rother, Carsten Heidelberg Univ Bergheimer Str 58 D-69115 Heidelberg Germany Harvard Univ Cambridge MA 02138 USA Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9780769549897

Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). this study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

关键词： Markov processes computer vision inference mechanisms integer programming minimisation random processes

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Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display

Principal Observation Ray Calibration for Tiled-Lens-Array I...

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

作者： Li, Weiming Wang, Haitao Zhou, Mingcai Wang, Shandong Jiao, Shaohui Mei, Xing Hong, Tao Lee, Hoyoung Kim, Jiyeun Samsung Adv Inst Technol China SAIT China Beijing Peoples R China SAIT Yongin South Korea CASIA NLPR Beijing Peoples R China

ISBN: (纸本)9780769549897

Integral imaging display (IID) is a promising technology to provide realistic 3D image without glasses. To achieve a large screen IID with a reasonable fabrication cost, a potential solution is a tiled-lens-array IID (TLA-IID). However, TLA-IIDs are subject to 3D image artifacts when there are even slight misalignments between the lens arrays. this work aims at compensating these artifacts by calibrating the lens array poses with a camera and including them in a ray model used for rendering the 3D image. Since the lens arrays are transparent, this task is challenging for traditional calibration methods. In this paper, we propose a novel calibration method based on defining a set of principle observation rays that pass lens centers of the TLA and the camera's optical center. the method is able to determine the lens array poses with only one camera at an arbitrary unknown position without using any additional markers. the principle observation rays are automatically extracted using a structured light based method from a dense correspondence map between the displayed and captured pixels. Experiments show that lens array misalignments can be estimated with a standard deviation smaller than 0.4 pixels. Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.

关键词： calibration cameras lenses liquid crystal displays pose estimation ray tracing realistic images rendering (computer graphics) screens (display) three-dimensional displays

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3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image

3D Visual Proxemics: Recognizing Human Interactions in 3D fr...

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

作者： Chakraborty, Ishani Cheng, Hui Javed, Omar SRI Int Princeton NJ 08540 USA

ISBN: (纸本)9780769549897

We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. 1 Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions. this approach has several advantages. First, it can accurately estimate relative distances and orientations between people in 3D. Second, it encodes spatial arrangements of people into a richer set of shape descriptors than afforded in 2D. Our 3D shape descriptors are invariant to camera pose variations often seen in web images and videos. the proposed approach also estimates camera pose and uses it to capture the intent of the photo. To achieve accurate 3D people layout estimation, we develop an algorithm that robustly fuses semantic constraints about human interpositions into a linear camera model. this enables our model to handle large variations in people size, heights (e. g. age) and poses. An accurate 3D layout also allows us to construct features informed by Proxemics that improves our semantic classification. To characterize the human interaction space, we introduce visual proxemes;a set of prototypical patterns that represent commonly occurring social interactions in events. We train a discriminative classifier that classifies 3D arrangements of people into visual proxemes and quantitatively evaluate the performance on a large, challenging dataset.

关键词： feature extraction human computer interaction image classification image sensors object recognition pose estimation shape recognition

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Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face recognition in the Wild

Fusing Robust Face Region Descriptors via Multiple Metric Le...

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

作者： Cui, Zhen Li, Wen Xu, Dong Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore Singapore Huaqiao Univ Sch Comp Sci Xiamen Peoples R China

ISBN: (纸本)9780769549897

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.

关键词： face recognition learning (artificial intelligence) principal component analysis

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Recognize Human Activities from Partially Observed Videos

Recognize Human Activities from Partially Observed Videos

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

作者： Cao, Yu Barrett, Daniel Barbu, Andrei Narayanaswamy, Siddharth Yu, Haonan Michaux, Aaron Lin, Yuewei Dickinson, Sven Siskind, Jeffrey Mark Wang, Song Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9780769549897

Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: 1) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.

关键词： image recognition image sequences probability video signal processing

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