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检索条件"机构=Computer Vision and Robotics Research"

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Visual Modules for Head Gesture Analysis in Intelligent Vehicle Systems

Visual Modules for Head Gesture Analysis in Intelligent Vehi...

引用

IEEE Symposium on Intelligent Vehicle

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper a coarse-to-fine system framework for analyzing the head gesture is presented. We discuss several important modules from computer vision aspects, including the pose-invariant face detection, face tracking, pose determination and high-resolution image reconstruction for eye pupils detection. Visual cues using intensity images obtained from in-car cameras are explored. A pose-invariant face detection algorithm is used to get the initial face area; afterwards face tracking and validation step is proposed to segment the face region for pose determination. The algorithm is tested on the drivers images under natural driving conditions. Experimental results show that the algorithm is robust to the head pose changes as well as the illumination changes. In this system framework, we propose that when coarse analysis utilizing the head pose alone is not sufficient for driver's behavior analysis, a finer analysis based on the eye gaze tracking is used, which requires images with sufficient resolution. A novel super-resolution reconstruction algorithm is proposed to help reveal more facial details, so as to facilitate the pupil detection. Experiment on the synthesis data shows the effectiveness of the super-resolution reconstruction algorithm

关键词： Intelligent vehicles Face detection Head Image analysis Reconstruction algorithms computer vision Image reconstruction Cameras Image segmentation Testing

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A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction

A Regression Model in TensorPCA Subspace for Face Image Supe...

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International Conference on Pattern Recognition

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

ISBN: (纸本)0769525210

A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images

关键词： Image resolution Image reconstruction Tensile stress Spatial resolution Principal component analysis Face detection Image analysis computer vision Robot vision systems Laboratories

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Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification

Panoramic Appearance Map (PAM) for Multi-camera Based Person...

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Tarak Gandhi Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

This paper proposes a concept of panoramic appearance map to perform reidentification of a people who leave the scene and reappear after some time. The map is a compact signature of appearance information of a person extracted from multiple cameras. The person is detected and tracked in multiple cameras and triangulation is used to accurately localize the person in 3-D. A virtual cylinder is formed around the person's location and mapped onto an image with the horizontal axis representing the azimuth angle and vertical axis representing the height. Each bin in the map image gets the appearance information from all the cameras which can observe it. The maps between different tracks are matched using a weighted metric. Experimental results showing person matching and reidentification show the effectiveness of the approach.

关键词： Cameras Data mining Azimuth Layout Surveillance Intelligent vehicles Color Histograms computer vision Robot vision systems

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Multimodal Voxelization and Kinematically Constrained Gaussian Mixture Models for Full Hand Pose Estimation: An Integrated Systems Approach

Multimodal Voxelization and Kinematically Constrained Gaussi...

引用

IEEE International Conference on computer vision Systems (ICVS)

作者： Shinko Y. Cheng M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

We propose a new approach to the hand pose estimation problem using only volume information. We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produced using shape-from-silhouette. We assume a 16-component, 27 degree-of-freedom kinematically constrained Gaussian mixture model, and fit it over the voxel images. We constrain the otherwise freely-arranged components of this model by a system of equations describing joint characteristics parameterized by component centroids and orientations. We use the EM algorithm and steepest descent to estimate the parameters of the model such that they yield the maximum likelihood and kinematically correct pose estimates. We demonstrate the effectiveness of the proposed system on synthesized as well as captured voxel images of the hand, and show that given appropriate initial conditions, the iterative model parameter estimation procedure effectively converges to and tracks the "voxelized" articulated hand.

关键词： Joints Biological system modeling Image segmentation Image reconstruction computer vision Image generation Parameter estimation Yield estimation Robot vision systems Laboratories

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A multimodal framework for vehicle and traffic flow analysis

A multimodal framework for vehicle and traffic flow analysis

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International Conference on Intelligent Transportation

作者： J. Ploetner M.M. Trivedi Computer Vision and Robotics Research Laboratory CVRR University of California San Diego La Jolla CA USA

This paper presents an overview of a novel multimodal system being developed at UC San Diego for vehicle detection and traffic flow analysis. A distributed multimodal array (DiMMA) framework is presented for sensory data acquisition, processing, analysis, fusion, and "active" control mechanisms needed to recognize objects, events, and activities which have multi-modal signatures. Current sensing modalities being researched include video, audio, seismic, magnetic, and passive infrared. Feature extraction and data fusion techniques are being investigated to improve robustness and study the advantages and disadvantages of each sensing modality. Preliminary results of this rapidly deployable system are discussed, along with possible future expansions, including laser range scanners, geophones, pneumatic road tubes, and traditional inductive loops

关键词： Vehicle detection Road vehicles Monitoring Robustness Sensor arrays Magnetic analysis Traffic control Information security Detectors Cameras

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Performance evaluation of a vision based lane tracker designed for driver assistance systems

Performance evaluation of a vision based lane tracker design...

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2005 IEEE Intelligent Vehicles Symposium

作者： McCall, Joel C. Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego

ISBN: (纸本)0780389611

Driver assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively research and even put into commercial production. It is therefore important to take a critical look at key aspects of these systems, one of which being lane position tracking. In this paper we present an analysis of lane position tracking in the context of driver support systems and examine previous research in this area. Using this analysis we present a lane tracking system designed to work well under a variety of road and environmental conditions. We examine what types of metrics are important for evaluating lane position accuracy for specific overall system objectives. A detailed quantitative evaluation of the system is presented in this paper using a variety of metrics and test conditions. © 2005 IEEE.

关键词： computer vision

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Looking-in and looking-out of a vehicle: Selected investigations in computer vision based enhanced vehicle safety

Looking-in and looking-out of a vehicle: Selected investigat...

引用

2005 IEEE International Conference on Vehicular Electronics and Safety

作者： Trivedi, Mohan M. Gandhi, Tarak McCall, Joel Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA United States

ISBN: (纸本)0780394356

This paper presents an overview of investigations into the role of computer vision technology in developing safer automobiles. We consider vision systems which can not only look out of the vehicle to detect and track roads, avoid hitting obstacles or pedestrian, but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer vision technology for safer automobiles is presented. We will consider three main components of the system, driver, vehicle, and vehicle surround. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. The paper includes discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent and activity patterns of drivers. © 2005 IEEE.

关键词： computer vision

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A binary tree for probability learning in eye detection

A binary tree for probability learning in eye detection

引用

2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Wu, Junwen Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San DiegoCA92037 United States

ISBN: (纸本)0769526608

In this paper we proposed to solve the eye detection and localization problem under a general statistical model based object detection framework. A binary tree representation is used to discover the objects' underlying statistical structure. Tree structures enable us to describe the object local statistical structure in a coarse-to-fine fashion. Each subtree explains the statistics for certain local *** tree is built in a top-down fashion. Subsets with negligible conditional independency are found by k-means clustering using mutual information. The conditionally independent features are separated into different subtrees, while more dependent features are tended to appear close in the tree. The distribution of the object can be learned accordingly. Gaussian mixture in the independent component analysis (ICA) subspace is used to model the distribution of each high dependent feature subset, where each independent component explains the local substructure. The use of tree structure enables us to learn the distribution recursively by applying Bayesian criterion. Substantial experiments were done to evaluate the performance over the eyes detection accuracy as well as the localization ability. Experimental results show a better detection accuracy than the Viisage system with a reasonable localization ability, which validate the algorithm. © 2005 IEEE computer Society. All rights reserved.

关键词： Object detection

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An integrated two-stage framework for robust head pose estimation

An integrated two-stage framework for robust head pose estim...

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2nd International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2005

作者： Wu, Junwen Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA 92093 United States

ISBN: (纸本)3540292292

Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated by analyzing finer geometrical structure details captured by bunch graphs. This coarse-to-fine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from -90° to 90° and the tilt angle spanning from -60° to 45°. The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Face recognition

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Vehicle mounted wide fov stereo for traffic and pedestrian detection

Vehicle mounted wide fov stereo for traffic and pedestrian d...

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IEEE International Conference on Image Processing 2005, ICIP 2005

作者： Gandhi, Tarak Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA 92093 United States

ISBN: (纸本)0780391349

This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles and pedestrians. The omni cameras are calibrated using sets of parallel lines on a parking lot. The calibration is used to rectify the omni images. Stereo matching is performed on the rectified images to detect other vehicles and pedestrians. Experimental results show promise of detecting these objects on the road. © 2005 IEEE.

关键词： Image processing

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