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Tracking multiple colored blobs with a moving camera

Tracking multiple colored blobs with a moving camera

引用

conference on computer vision and pattern recognition (cvpr)

作者： A.A. Argyros M.I.A. Lourakis Foundation for Research and Technology-Hellas Vassilika Vouton Institute for Computer Science Crete Greece

This paper concerns a method for tracking multiple blobs exhibiting certain color distributions in images acquired by a possibly moving camera. The method encompasses a collection of techniques that enable modeling and detecting the blobs possessing the desired color distribution(s), as well as inferring their temporal association across image sequences. Appropriately colored blobs are detected with a Bayesian classifier, which is bootstrapped with a small set of training data. Then, an online iterative training procedure is employed to refine the classifier using additional training images. Online adaptation of color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique, which can handle multiple colored blobs. Such blobs may move in complex trajectories and occlude each other in the field of view of a possibly moving camera, while their number may vary over time. A prototype implementation of the developed system running on a conventional Pentium IV processor at 2.5 GHz operates on 320/spl times/240 live video in real time (30Hz). It is worth pointing out that currently, the cycle time of the tracker is determined by the maximum acquisition frame rate that is supported by our ieee 1394 camera, rather than the latency introduced by the computational overhead for tracking blobs.

关键词： Cameras Humans Lighting Face detection Fingers Streaming media Tracking computer science Image sequences Bayesian methods

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Detecting and Tracking Eyes Through Dynamic Terrain Feature Matching

Detecting and Tracking Eyes Through Dynamic Terrain Feature ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jun Wang Lijun Yin Department of Computer Science State University of New York Binghamton Binghamton NY USA

Automatic eye detection and tracking is an important component in the advanced human-computer interface design. In this paper, we present a novel approach for detecting and tracking eyes through matching their terrain features. Regarded as a 3D terrain surface, eye region exhibits certain intrinsic traits when using a so-called topographic representation. With the topographic classification of terrain features, we generate a terrain map for each facial image and extract eye candidates from the terrain map. Our algorithm mainly consists of two parts. First, eye locations are estimated from the candidate positions using an appearancebased object recognition technique. Second, a mutual information based fitting function is defined to describe the similarity between two terrain surfaces. By optimizing the fitting function, eye locations are updated for each frame in a video sequence. The distinction of the proposed approach lies in that both eye detection and eye tracking are performed in a terrain map domain rather than an original intensity image domain. The robustness of the approach is demonstrated under various imaging conditions and with different facial appearances using a web camera.

关键词： Eyes Surface topography Surface fitting Data mining Mutual information Robustness Cameras Object detection Humans computer science

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Particle filtering for geometric active contours with application to tracking moving and deforming objects

Particle filtering for geometric active contours with applic...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yogesh Rathi N. Vaswani A. Tannenbaum A. Yezzi School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Geometric active contours are formulated in a manner which is parametrization independent. As such, they are amenable to representation as the zero level set of the graph of a higher dimensional function. This representation is able to deal with singularities and changes in topology of the contour. It has been used very successfully in static images for segmentation and registration problems where the contour (represented as an implicit curve) is evolved until it minimizes an image based energy functional. But tracking involves estimating the global motion of the object and its local deformations as a function of time. Some attempts have been made to use geometric active contours for tracking, but most of these minimize the energy at each frame and do not utilize the temporal coherency of the motion or the deformation. On the other hand, tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape. But these are dependent on the chosen parametrization and cannot handle changes in curve topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects.

关键词： Active filters Filtering Active contours Particle tracking Topology Level set Image segmentation Motion estimation Particle filters Shape

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Recognizing facial expression: machine learning and application to spontaneous behavior

Recognizing facial expression: machine learning and applicat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.S. Bartlett G. Littlewort M. Frank C. Lainscsek I. Fasel J. Movellan Institute for Neural Computation University of California San Diego USA Rutgers University New Brunswick NJ USA

We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of AdaBoost for feature selection prior to classification by SVM or LDA. Best results were obtained by selecting a subset of Gabor filters using AdaBoost followed by classification with support vector machines. The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics. We applied the system to to fully automated recognition of facial actions (FACS). The present system classifies 17 action units, whether they occur singly or in combination with other actions, with a mean accuracy of 94.8%. We present preliminary results for applying this system to spontaneous facial expressions.

关键词： Face recognition Machine learning Support vector machines Support vector machine classification Linear discriminant analysis Learning systems Engines Gabor filters Real time systems Time measurement

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Bayesian object detection in dynamic scenes

Bayesian object detection in dynamic scenes

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Sheikh M. Shah Computer Vision Laboratory School of Computer Science University of Central Florida Orlando FL USA

Detecting moving objects using stationary cameras is an important precursor to many activity recognition, object recognition and tracking algorithms. In this paper, three innovations are presented over existing approaches. Firstly, the model of the intensities of image pixels as independently distributed random variables is challenged and it is asserted that useful correlation exists in the intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of nominal camera motion and dynamic textures. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, multi-modal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. Secondly, temporal persistence is proposed as a detection criteria. Unlike previous approaches to object detection which detect objects by building adaptive models of the only background, the foreground is also modeled to augment the detection of objects (without explicit tracking) since objects detected in a preceding frame contain substantial evidence for detection in a current frame. Third, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of pixel-wise labeling and the posterior function is maximized efficiently using graph cuts. Experimental validation of the proposed method is presented on a diverse set of dynamic scenes.

关键词： Bayesian methods Object detection Layout Pixel Cameras Object recognition Technological innovation Random variables Motion detection Uncertainty

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Radon-based structure from motion without correspondences

Radon-based structure from motion without correspondences

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conference on computer vision and pattern recognition (cvpr)

作者： A. Makadia C. Geyer S. Sastry K. Daniilidis University of Pennsylvania Philadelphia PA USA University of California Berkeley Berkeley CA USA

We present a novel approach for the estimation of 3D-motion directly from two images using the Radon transform. We assume a similarity function defined on the cross-product of two images which assigns a weight to all feature pairs. This similarity function is integrated over all feature pairs that satisfy the epipolar constraint. This integration is equivalent to filtering the similarity function with a Dirac function embedding the epipolar constraint. The result of this convolution is a function of the five unknown motion parameters with maxima at the positions of compatible rigid motions. The breakthrough is in the realization that the Radon transform is a filtering operator: If we assume that images are defined on spheres and the epipolar constraint is a group action of two rotations on two spheres, then the Radon transform is a convolution/correlation integral. We propose a new algorithm to compute this integral from the spherical harmonics of the similarity and Dirac functions. The resulting resolution in the motion space depends on the bandwidth we keep from the spherical transform. The strength of the algorithm is in avoiding a commitment to correspondences, thus being robust to erroneous feature detection, outliers, and multiple motions. The algorithm has been tested in sequences of real omnidirectional images and it outperforms correspondence-based structure from motion.

关键词： Convolution Filtering computer vision Motion estimation Cameras Kernel Power harmonic filters Bandwidth Robustness Motion detection

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A projector-camera system with real-time photometric adaptation for dynamic environments

A projector-camera system with real-time photometric adaptat...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Fujii M.D. Grossberg S.K. Nayar NTT Cyber Space Laboratories NTT Corporation Yokosuka Japan Department Computer Science City College and Graduate Center City University of New York New York NY USA Department Computer Science Columbia University New York NY USA

Projection systems can be used to implement augmented reality, as well as to create both displays and interfaces on ordinary surfaces. Ordinary surfaces have varying reflectance, color, and geometry. These variations can be accounted for by integrating a camera into the projection system and applying methods from computer vision. The methods currently applied are fundamentally limited since they assume the camera, projector, and scene are static. In this paper, we describe a technique for photometrically adaptive projection that makes it possible to handle a dynamic environment. We begin by presenting a co-axial projector-camera system whose geometric correspondence is independent of changes in the environment. To handle photometric changes, our method uses the errors between the desired and measured appearance of the projected image. A key novel aspect of our algorithm is that we combine a physics-based model with dynamic feedback to achieve real time adaptation to the changing environment. We verify our algorithm through a wide variety of experiments. We show that it is accurate and runs in real-time. Our algorithm can be applied broadly to assist HCI, visualization, shape recovery, and entertainment applications.

关键词： Cameras Photometry Real time systems Machine vision computer displays

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A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples

A framework of 2D Fisher discriminant analysis: application ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： H. Kong L. Wang E.K. Teoh J.-G. Wang R. Venkateswarlu Nanyang Technological University Singapore Institute for Infocomm Research Singapore

A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.

关键词： Face recognition Null space Linear discriminant analysis Scattering Image databases Covariance matrix Eigenvalues and eigenfunctions Vectors Principal component analysis Feature extraction

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Statistical cue integration for foveated wide-field surveillance

Statistical cue integration for foveated wide-field surveill...

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conference on computer vision and pattern recognition (cvpr)

作者： S.J.D. Prince J.H. Elder Y. Hou M. Sizintsev Y. Olevskiy Centre for Vision Research York University Toronto ONT Canada

Reliable wide-field detection of human activity is an unsolved problem. The main difficulty is that low resolution and the unconstrained nature of realistic environments and human behaviour make form cues unreliable. Here we argue that reliability in far- or wide-field detection can still be achieved by probabilistic combination of multiple weak but complementary visual cues that do not depend on detailed form analysis. To demonstrate, we describe a real-time Bayesian algorithm for localizing human activity in relatively unconstrained scenes, using motion, background subtraction and skin colour cues. Fast sampling of scale space is achieved using integral images and a flexible norm that can handle sparse cues without loss of statistical power. We show that the probabilistic approach far outperforms a representative logical approach in which skin and background subtraction classifiers are combined conjunctively. Our method is currently used in a pre-attentive human activity sensor, generating saccadic targets for an attentive foveated vision system that reliably fixates faces over a 130 deg field of view, allowing high-resolution capture of facial images over a large dynamic scene.

关键词： Surveillance Humans Layout Skin Bayesian methods Image sampling Image sensors Sensor systems Machine vision Power system reliability

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A dense stereo matching using two-pass dynamic programming with generalized ground control points

A dense stereo matching using two-pass dynamic programming w...

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conference on computer vision and pattern recognition (cvpr)

作者： Jae Chul Kim Kyoung Mu Lee Byoung Tae Choi Sang Uk Lee Electronics and Telecommunications Research Institute Daejeon South Korea School of Electrical Engineering ASRI Seoul National University Seoul South Korea

ISBN: (纸本)0769523722

A method for solving dense stereo matching problem is presented in this paper. First, a new generalized ground control points (GGCPs) scheme is introduced, where one or more disparity candidates for the true disparity of each pixel are assigned by local matching using the oriented spatial filters. By allowing "all" pixels to have multiple candidates for their true disparities, GGCPs not only guarantee to provide a sufficient number of starting pixels needed for guiding the subsequent matching process, but also remarkably reduce the risk of false match, improving the previous GCP-based approaches where the number of the selected control points tends to be inversely proportional to the reliability. Second, by employing a two-pass dynamic programming technique that performs optimization both along and across the scanlines, we solve the typical inter-scanline inconsistency problem. Moreover, combined with the GGCPs, the stability and efficiency of the optimization are improved significantly. Experimental results for the standard data sets show that the proposed algorithm achieves comparable results to the state-of-the-arts with much less computational cost.

关键词： Dynamic programming Stereo vision Telecommunication control Spatial filters computer vision Proportional control Stability Computational efficiency Application software Degradation

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