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A Comparative Study of Boosted and Adaptive Particle Filters for Affine-Invariant Target Detection and Tracking

A Comparative Study of Boosted and Adaptive Particle Filters...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Guoliang Fan V. Venkataraman Li Tang J. Havlicek School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA School of Electrical and Computer Engineering University of Oklahama Norman OK USA

This paper addresses a specific multi-aspect target detection and tracking problem where the dynamics of the target’s aspect is modeled by an affine model following a first-order Markov process. We are interested in how to achieve robust and accurate Monte Carlo estimation in a high-dimensional state space with poor target visibility by re-visiting two recent improvements to particle filters, i.e., "boosting" and "adapting". The impetus of this work is a tracking indicator that estimates the tracking performance based on the observation model and may trigger either one of two actions when it is necessary. One is "boosting", i.e., the detector specified by the tracker’s previous output is involved to induce more promising particles, and the original idea of "boosting" is extended here by encouraging positive interaction between the detector and the tracker. The other is "adapting", i.e., the system model can self-adjust to enhance the tracking capability. We compare two methods in the context of affine-invariant target tracking and with respect to their contributions to improve the particle quality. Experiments on simulated image sequences with real infrared background show that both techniques can improve the tracking performance by balancing the focus and the diversity of particle distribution.

关键词： Particle filters Object detection Target tracking Particle tracking Boosting Detectors Markov processes Robustness Monte Carlo methods State estimation

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Robust Video-Based Surveillance by Integrating Target Detection with Tracking

Robust Video-Based Surveillance by Integrating Target Detect...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Junxian Wang G. Bebis R. Miller Computer Vision Laboratory University of Nevada Reno NV USA Vehicle Design R&A Department Ford Motor Company Limited Dearborn MI USA

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where detection and tracking are addressed simultaneously in a unified framework (i.e., detection results trigger tracking, and tracking re-enforces detections)to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other (i.e., "detect-then-track"), we feed the results of tracking back to the detection stage to adaptively optimize the threshold used in the detection stage and improve system robustness (i.e., "detect-and-track"). Specifically, the initial locations and representations of the targets are extracted by background subtraction. To model the background, we employ Support Vector Regression (SVR) along with an on-line learning scheme to update it efficiently over time. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and achieve a high shape matching confidence level. Feeding back the results of tracking to the detection stage restricts the range of threshold values, suppress false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing projection distortions. We have validated the proposed framework by detecting vehicles and pedestrians in traffic scenes using both visible and thermal video sequences. Experimental results and comparisons with framebased detection and kernel-based tracking methods illustrate the robustness of our approach.

关键词： Robustness Surveillance Object detection Target tracking Shape Vehicle detection Target recognition Feeds Optimization methods Histograms

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Level Set Segmentation Using Statistical Shape Priors

Level Set Segmentation Using Statistical Shape Priors

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A.S. El-Baz A.A. Farag H.A. El Munim S.E. Yuksel Computer Vision & Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

A novel and robust 3-D segmentation approach is proposed using level sets based on shape constraints. The approach depends on both the gray level and shape information. A partial differential equation (PDE) is developed to control the evolution of the level sets. The PDE does not include weighting coefficients to be tuned, overcoming the disadvantages of other PDE approaches. The shape information is gathered from a set of the signed distance maps representing the training data as a histogram of the occurrences of the points inside and outside the object. We use a novel statistical approach to get a probability density function (pdf) for the signed distance map of the points inside and outside and also the distribution of gray level inside and outside the object. The proposed statistical approach is based on modelling the empirical density function (normalized histogram of occurrence) for either the gray level distribution or signed distance map with a linear combination of Gaussians (LCG) with positive and negative components. We modify an Expectation-Maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation for the modified EM algorithm to start with. The pdf’s of the signed distance and intensity gray level are embedded in the speed function of the level set specifying the direction of evolution. Experimental results show how the approach is accurate in segmenting different types of 2-D and 3-D data sets including medical images.

关键词： Level set Shape Histograms Approximation algorithms Robustness Partial differential equations Training data Probability density function Density functional theory Gaussian distribution

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Characterizing fMRI Activations within Regions of Interest (ROIs) Using 3D Moment Invariants

Characterizing fMRI Activations within Regions of Interest (...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： B. Ng R. Abugharbieh Xuemei Huang M.J. McKeown Department of Electrical and Computer Engineering Pacific Parkinson's Research Center University of British Columbia Vancouver BC Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada Department of Neurology School of Medicine University of North Carolina Chapel Hill NC USA Department of Medicine (Neurology) Pacific Parkinson's Research Center University of British Columbia Vancouver BC Canada

A novel method is proposed for characterizing spatial distribution changes in functional magnetic resonance imaging (fMRI) activation statistics under different experimental conditions. The proposed technique, based on three dimensional (3D) invariant moment descriptors, was applied to fMRI data recorded from eight healthy subjects performing internally or externally-cued finger tapping sequences. Voxel-based activation statistics were characterized in several regions of interest (ROIs), including the supplementary motor area (SMA), cerebellum, primary motor cortex, prefrontal cortex, and caudate. Examining the activation patterns of these neural regions using 3D moment invariants revealed that the patterns of activation regions were significantly different during externally and internally cued tasks when computed across subjects. In contrast, traditional methods that are based on amplitude of the activation statistics demonstrated reduced discriminability. The results suggest that the spatial distribution of activation is a more sensitive measure of activation changes, and complements conventional fMRI analyses.

关键词： Magnetic heads Filtering Low pass filters Nonlinear filters Linear discriminant analysis Biomedical imaging Nervous system Fingers Coils Smoothing methods

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Partial & Holistic Face recognition on FRGC-II data using Support Vector Machine

Partial & Holistic Face Recognition on FRGC-II data using Su...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Savvides R. Abiantun J. Heo S. Park C. Xie B.V.K. Vijayakumar Electrical & Computer Eng Department Carnegie Mellon University Pittsburgh PA USA

In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face recognition Grand Challenge(FRGC) phase-II data which deals with subjects captured under uncontrolled conditions such as harsh overhead illumination, some pose variations and facial expressions in both indoor and outdoor environments. Other variations include the presence and absence of eye-glasses. The database consists of a generic dataset of 12,776 images for training a generic face subspace, a target set of 16,028 images and a query set of 8,014 images are given for matching. We propose to use our novel face recognition algorithm using Kernel Correlation Feature Analysis for dimensionality reduction (222 features) coupled with Support Vector Machine discriminative training in the Target KCFA feature set for providing a similarity distance measure of the probe to each target subject. We show that this algorithm configuration yields the best verification rate at 0.1% FAR (87.5%) compared to PCA+SVM, GSLDA+SVM, SVM+SVM, KDA+SVM. Thus we explore with our proposed algorithm which facial regions provide the best discrimination ability, we analyze performing partial face recognition using the eye-region, nose region and mouth region. We empirically find that the eye-region is the most discriminative feature of the faces in FRGC data and yields a verification rate closest to the holistic face recognition of 83.5% @ 0.1% FAR compared to 87.5%. We use Support Vector Machines for fusing these two to boost the performance to ~90@0.1 % FAR on the first large-scale face database such as the FRGC dataset.

关键词： Face recognition Support vector machines Spatial databases Algorithm design and analysis Lighting Image databases Kernel Probes Performance analysis Nose

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Specifying, Interpreting and Detecting High-level, Spatio-Temporal Composite Events in Single and Multi-Camera Systems

Specifying, Interpreting and Detecting High-level, Spatio-Te...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Velipasalar L.M. Brown A. Hampapur Electrical Engineering Department Princeton University Princeton NJ USA IBM Thomas J. Watson Research Center Hawthorne NY USA

Detecting and tracking moving objects are important and challenging problems which have attracted much attention from the research community. However, in most cases, it is not enough to only track the objects. The goal should be to detect the occurrences of events of interest, which is important for applications such as video surveillance, video browsing and indexing. Yet, event detection introduces the challenge of providing the flexibility to specify customized events with varying complexity, and entering them to a system in a generic way. The event definitions should not be pre-defined and hard-coded. We introduce a spatio-temporal event detection system which lets the users to specify multiple composite events of high-complexity, and then detects their occurrence automatically. Events can be defined on a single camera view or across multiple camera views. Semantically higher level event scenarios can be built by using the building blocks, which we call the primitive events, and combining them by operators. More importantly, the newly defined composite events can be combined with each other. This layered structure makes the definition of events with higher and higher complexity possible. The event definitions are written to an XML file, which is then parsed and communicated to the tracking engines running on the videos of the corresponding cameras. With the proposed system, we have reached the next level and managed to go from detecting "a person exiting the building" to detecting "a person coming from the south corridor of the building and then exiting the building".

关键词： Event detection Object detection Cameras Video surveillance computer languages Indexing XML Engines Statistics Monitoring

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Germination of the Active Lighting : An Introduction and Brief History of Our Research

Germination of the Active Lighting : An Introduction and Bri...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Hiura K. Sato I. Kanaya Osaka University Toyonaka Osaka Japan

We have been engaged in the researches on the active lighting techniques for twenty years. At first, we proposed a method to measure the 3-D shape of the object by projecting multiple patterns of structured light, because it is much faster than the method with scanning slit of light, and dense range image can be measured. For this purpose, we made a projector which consists of mechanicallychangeable masks, but it was soon replaced by a speciallymade liquid crystal panel with many slit segments. From the fact, we can proudly declare that the projector is not a diverted device but exclusively developed as a part of projector-camera systems, and the projection target is originally not a planar screen but a real scene with varied depth. Actually, this infant device was successful and commercially enough practical for 3-D shape measurement, but unfortunately we had to wait for the evolution of panel display technology more than a decade, to make a step to the studies which utilizes full duality between cameras and projectors. In the middle of 1990s, many researchers started using video projectors. Since we were computer vision researchers, we used the flexibility of video projector not for a interaction technique but improving performance of 3-D measurement such as motion tracking. But we were also aware of its advantage as a visualization tool in a real scene. Alike to the other research activities, we have confirmed that the projector can change the seen color, texture and specular of the real object. In addition, faked shade projected on the object make it possible to change our interpretation of its shape. Furthermore, the traditional shape measurement method we had developed is seamlessly combined with the visualization technique. Therefore, we have a common view to the possibility of intuitive interface systems with realistic visualization for communicating, training and designing works, so now is a good time to look-ing back on the history of projector-camera syst

关键词： History Shape measurement Visualization Layout Liquid crystals Image segmentation computer displays Cameras computer vision Motion measurement

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workshop on computer vision Applications for the Visually Impaired - Table of contents

Workshop on Computer Vision Applications for the Visually Im...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

Presents the table of contents of the proceedings.

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IEEE workshop on Machine vision for Intelligent Vehicles - Table of contents

IEEE Workshop on Machine Vision for Intelligent Vehicles - T...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

Presents the table of contents of the proceedings.

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Strategies and Benefits of Fusion of 2D and 3D Face recognition

Strategies and Benefits of Fusion of 2D and 3D Face Recognit...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Husken M. Brauckmann S. Gehlen C. Von der Malsburg Viisage Technology AG Bochum Germany Institut für Neuroinformatik Ruhr Universität Bochum Bochum Germany

The extension of 2D image-based face recognition methods with respect to 3D shape information and the fusion of both modalities is one of the main topics in the recent development of facial recognition. In this paper we discuss different strategies and their expected benefit for the fusion of 2D and 3D face recognition. The face recognition grand challenge (FRGC) provides for the first time ever a public benchmark dataset of a suitable size to evaluate the accuracy of both 2D and 3D face recognition. We use this benchmark to evaluate hierarchical graph matching (HGM), an universal approach to 2D and 3D face recognition, and demonstrate the benefit of different fusion strategies. The results show that HGM yields the best results presented at the recent FRGC workshop, that 2D face recognition is significantly more accurate than 3D face recognition and that the fusion of both modalities leads to a further improvement of the 2D results.

关键词： Face recognition Shape Face detection pattern recognition Image databases Gray-scale Color computer vision Data security Access control

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