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

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Face Tracking and recognition with Visual Constraints in Real-World Videos

Face Tracking and Recognition with Visual Constraints in Rea...

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

作者： Minyoung Kim Sanjiv Kumar Vladimir Pavlovic Henry Rowley Department of Computer Science Rutgers University Piscataway NJ USA Google Research New York NY USA Google Research Mountain View CA USA

We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework. the generative term conforms the particles to the space of generic face poses while the discriminative one ensures rejection of poorly aligned targets. this leads to a tracker that significantly improves robustness against abrupt appearance changes and occlusions, critical for the subsequent recognition phase. Identity of the tracked subject is established by fusing pose-discriminant and person-discriminant features over the duration of a video sequence. this leads to a robust video-based face recognizer with state-of-the-art recognition performance. We test the quality of tracking and face recognition on real-world noisy videos from YouTube as well as the standard Honda/UCSD database. Our approach produces successful face tracking results on over 80% of all videos without video or person-specific parameter tuning. the good tracking performance induces similarly high recognition rates: 100% on Honda/UCSD and over 70% on the YouTube set containing 35 celebrities in 1500 sequences.

关键词： Face recognition Videos Target tracking Lighting Face detection Robustness Particle tracking Filtering YouTube computer science

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Locally Assembled Binary (LAB) Feature with Feature-centric Cascade for Fast and Accurate Face Detection

Locally Assembled Binary (LAB) Feature with Feature-centric ...

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

作者： Shengye Yan Shiguang Shan Xilin Chen Wen Gao Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy of Sciences (CAS) Beijing China School of EE& Peking University Beijing China Digital Media Research Center Institute of Computing Technology Chinese Academy and Sciences Beijing China

In this paper, we describe a novel type of feature for fast and accurate face detection. the feature is called Locally Assembled Binary (LAB) Haar feature. LAB feature is basically inspired by the success of Haar feature and Local Binary pattern (LBP) for face detection, but it is far beyond a simple combination. In our method, Haar features are modified to keep only the ordinal relationship (named by binary Haar feature) rather than the difference between the accumulated intensities. Several neighboring binary Haar features are then assembled to capture their co-occurrence with similar idea to LBP. We show that the feature is more efficient than Haar feature and LBP both in discriminating power and computational cost. Furthermore, a novel efficient detection method called feature-centric cascade is proposed to build an efficient detector, which is developed from the feature-centric method. Experimental results on the CMU+MIT frontal face test set and CMU profile test set show that the proposed method can achieve very good results and amazing detection speed.

关键词： Assembly Face detection computer vision Detectors Content addressable storage Computational efficiency Testing Intelligent robots Humans Skin

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Auto-context and Its Application to High-level vision Tasks

Auto-context and Its Application to High-level Vision Tasks

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

作者： Zhuowen Tu Lab of Neuro Imaging University of California Los Angeles USA

the notion of using context information for solving high-level vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. the current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. In this paper, we propose an auto-context algorithm. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. the discriminative probability (or classification confidence) maps by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. the algorithm then iterates to approach the ground truth. Auto-context learns an integrated low-level and context model, and is very general and easy to implement. Under nearly the identical parameter setting in the training, we apply the algorithm on three challenging vision applications: object segmentation, human body configuration, and scene region labeling. It typically takes about 30 ～ 70 seconds to run the algorithm in testing. Moreover, the scope of the proposed algorithm goes beyond high-level vision. It has the potential to be used for a wide variety of problems of multi-variate labeling.

关键词： Shape Layout Context modeling Markov random fields Hidden Markov models Biological system modeling Labeling Statistics Neuroimaging Algorithm design and analysis

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Margin-Based Discriminant Dimensionality Reduction for Visual recognition

Margin-Based Discriminant Dimensionality Reduction for Visua...

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

作者： Hakan Cevikalp Frederic Jurie Bill Triggs Robi Polikar Eskisehir Osmangazi University Eskisehir Turkey Laboratoire Jean Kuntzmann Grenoble France University of Caen France Rowan University Glassboro NJ USA

Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the class regions densely. In such cases, test samples often fall into gaps between training samples where the nearest neighbours are too distant to be good indicators of class membership. One solution is to project the data onto a discriminative lower dimensional subspace. We propose a gap-resistant nonparametric method for finding such subspaces: first the gaps are filled by building a convex model of the region spanned by each class - we test the affine and convex hulls and the bounding disk of the class training samples - then a set of highly discriminative directions is found by building and decomposing a scatter matrix of weighted displacement vectors from training examples to nearby rival class regions. the weights are chosen to focus attention on narrow margin cases while still allowing more diversity and hence more discriminability than the 1D linear Support Vector Machine (SVM) projection. Experimental results on several face and object recognition datasets show that the method finds effective projections, allowing simple classifiers such as nearest neighbours to work well in the low dimensional reduced space.

关键词： Testing Scattering Kernel Matrix decomposition Support vector machines Support vector machine classification Face recognition Object recognition Multidimensional systems Filling

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Conditional Density Learning via Regression with Application to Deformable Shape Segmentation

Conditional Density Learning via Regression with Application...

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

作者： Jingdan Zhang Shaohua Kevin Zhou Dorin Comaniciu Leonard McMillan Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA Department of Computer Science University of North Carolina Chapel Hill Chapel Hill NC USA

Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C|I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities of the model. We propose to learn a conditional probability density satisfying these desired properties for the given training data set. To do this, we formulate a novel regression problem that finds a function approximating the target density. Learning the regressor is challenging due to the high dimensionality of model parameters, C, and the complexity of relating the image and the model. Our approach makes two contributions. First, we take a multi-level refinement approach by learning a series of density functions, each of which guides the solution of optimization algorithms increasingly converging to the correct solution. Second, we propose a new data sampling algorithm that takes into account the gradient information of the target function. We have applied this learning approach to deformable shape segmentation and have achieved better accuracy than the previous methods.

关键词： Image segmentation Application software Density functional theory Image converters Training data Facial features Active shape model Data systems computer science Probability density function

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Human Action recognition using Local Spatio-Temporal Discriminant Embedding

Human Action Recognition using Local Spatio-Temporal Discrim...

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

作者： Kui Jia Dit-Yan Yeung Shenzhen Institute of Advanced Integration Technology CAS/CUHK Shenzhen China Hong Kong University of Science and Technology Hong Kong China

Human action video sequences can be considered as nonlinear dynamic shape manifolds in the space of image frames. In this paper, we address learning and classifying human actions on embedded low-dimensional manifolds. We propose a novel manifold embedding method, called Local Spatio-Temporal Discriminant Embedding (LSTDE). the discriminating capabilities of the proposed method are two-fold: (1) for local spatial discrimination, LSTDE projects data points (silhouette-based image frames of human action sequences) in a local neighborhood into the embedding space where data points of the same action class are close while those of different classes are far apart;(2) in such a local neighborhood, each data point has an associated short video segment, which forms a local temporal subspace on the embedded manifold. LSTDE finds an optimal embedding which maximizes the principal angles between those temporal subspaces associated with data points of different classes. Benefiting from the joint spatio-temporal discriminant embedding, our method is potentially more powerful for classifying human actions with similar space-time shapes, and is able to perform recognition on a frame-by-frame or short video segment basis. Experimental results demonstrate that our method can accurately recognize human actions, and can improve the recognition performance over some representative manifold embedding methods, especially on highly confusing human action types.

关键词： Humans Shape Space technology Video sequences Learning systems Information analysis computer vision Optical computing Image recognition Content addressable storage

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Learning Object Motion patterns for Anomaly Detection and Improved Object Detection

Learning Object Motion Patterns for Anomaly Detection and Im...

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

作者： Arslan Basharat Alexei Gritai Mubarak Shah Computer Vision Lab School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. the proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach.

关键词： Object detection Motion detection Layout Surveillance Feedback Cameras Pipelines Event detection Probability density function Videos

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Max Margin AND/OR Graph Learning for Parsing the Human Body

Max Margin AND/OR Graph Learning for Parsing the Human Body

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

作者： Long (Leo) Zhu Yuanhao Chen Yifei Lu Chenxi Lin Alan Yuille Department of Statistics University of California Los Angeles USA University of Science and Technology China Shanghai Jiaotong University China Microsoft Research Asia China Department of Statistics Psychology and Computer Science University of California Los Angeles USA

We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human body and its parts by an AND/OR graph, which is a multi-level mixture of Markov Random Fields (MRFs). Max-margin learning, which is a generalization of the training algorithm for support vector machines (SVMs), is used to learn the parameters of the AND/OR graph model discriminatively. there are four advantages from this combination of AND/OR graphs and max-margin learning. Firstly, the AND/OR graph allows us to handle enormous articulated poses with a compact graphical model. Secondly, max-margin learning has more discriminative power than the traditional maximum likelihood approach. thirdly, the parameters of the AND/OR graph model are optimized globally. In particular, the weights of the appearance model for individual nodes and the relative importance of spatial relationships between nodes are learnt simultaneously. Finally, the kernel trick can be used to handle high dimensional features and to enable complex similarity measure of shapes. We perform comparison experiments on the baseball datasets, showing significant improvements over state of the art methods.

关键词： Humans Biological system modeling Machine learning Tree graphs Statistics Maximum likelihood estimation Kernel Shape measurement Performance analysis computer vision

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Principled Fusion of High-level Model and Low-level Cues for Motion Segmentation

Principled Fusion of High-level Model and Low-level Cues for...

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

作者： Arasanathan thayananthan Masahiro Iwasaki Roberto Cipolla Department of Engineering University of Cambridge Cambridge UK Panasonic Europe Limited Cambridge UK

High-level generative models provide elegant descriptions of videos and are commonly used as the inference framework in many unsupervised motion segmentation schemes. However, approximate inference in these models often require ad-hoc initialization to avoid local minima issues. Low-level cues, obtained independently from the high-level model, can constrain the search space and reduce the chance of inference algorithms falling into a local minima. this paper introduces a novel principled fusion framework where, local hierarchical superpixels segmentation of images are used to capture local motion. the low-level cues such as local motion, on their own, not adequate to obtain full motion segmentation as occlusion needs to be handled globally. We fuse the low-level motion cues with the high-level model in a principled manner to surmount the shortcomings of using only the high-level model or low-level cues to perform motion segmentation. the fused model contains both continuous and discrete variables which forms a number of Markov Random fields. Variational approximation or belief propagation algorithms cannot be applied due to the complex interactions between the variables. Hence, approximate inference is performed using expectation propagation (EP) algorithm. the scheme is demonstrated by performing motion segmentation in two video sequences.

关键词： Motion segmentation computer vision Inference algorithms Fusion power generation Videos Image segmentation Fuses Markov random fields Belief propagation Approximation algorithms

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Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform

Spatio-temporal Saliency Detection Using Phase Spectrum of Q...

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

作者： Chenlei Guo Qi Ma Liming Zhang Department of Electronic Engineering Fudan University Shanghai China

Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), Neuromorphic vision Toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. the results are good, but the reason is questionable. In this paper, we propose it is the phase spectrum, not the amplitude spectrum, of the Fourier transform that is the key in obtaining the location of salient areas. We provide some examples to show that PFT can get better results in comparison with SR and requires less computational complexity as well. Furthermore, PFT can be easily extended from a two-dimensional Fourier transform to a Quaternion Fourier Transform (QFT) if the value of each pixel is represented as a quaternion composed of intensity, color and motion feature. the added motion dimension allows the phase spectrum to represent spatio-temporal saliency in order to engage in attention selection for videos as well as images. Extensive tests of videos, natural images and psychological patterns show that the proposed method is more effective than other models. Moreover, it is very robust against white-colored noise and meets the real-time requirements, which has great potentials in engineering applications.

关键词： Phase detection Quaternions Fourier transforms Eyes Strontium Layout Humans Object detection computer vision Computational modeling

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