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检索条件"机构=The Computer Vision And Robotics Research Laboratory"

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A Binary Tree for Probability Learning in Eye Detection

A Binary Tree for Probability Learning in Eye Detection

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

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 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 substructure. The 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.

关键词： Binary trees Object detection Bayesian methods computer vision Eyes Tree data structures Probability Statistical distributions Independent component analysis Face detection

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Performance characterization for Gaussian mixture model based motion detection algorithms

Performance characterization for Gaussian mixture model base...

引用

IEEE International Conference on Image Processing

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

Pixelwise Gaussian mixture based background modeling algorithm proposed in S. Stauffer and W.E.L. Grimson (1999 and 2000) has been proved to be robust for many motion detection applications. However, the algorithm is not sensitive to fast motion. One possible solution is to introduce local correlations. Starting from this, this paper proposes to use the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background. Experimental evaluation validates the algorithm. Motivated by the demands of selecting the more appropriate algorithm for a specific application, qualitative and quantitative performance comparisons of these two approaches are presented. We proposes three metrics. One is to characterize the pixel level accuracy and the other two are to evaluate the errors in the object level. Pros and cons of both algorithms are summarized.

关键词： Motion detection computer vision Noise robustness Guidelines Statistical distributions Robot sensing systems Robot vision systems Laboratories Application software computer errors

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Dynamic Panoramic Surround Map: Motivation and Omni Video Based Approach

Dynamic Panoramic Surround Map: Motivation and Omni Video Ba...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

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

Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. Surround information or maps can help in ethnographic studies of driver behavior as well as provide a critical input in the development of effective driver assistance system. In this paper, we introduce the concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle, and detects the objects of importance on the road. Omnidirectional cameras which give a panoramic view of the surroundings can be useful for visualizing and analyzing the nearby surroundings of the vehicle. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni-directional cameras on the vehicle is developed. Successful generation of DPS in experimental runs on an instrumented vehicle testbed is demonstrated. These experiments prove the basic feasibility and show promise of omni video based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle.

关键词： Vehicle dynamics Vehicle detection Cameras Vehicle driving Vehicle safety Automobiles Road vehicles Object detection Visualization Motion analysis

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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

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

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

This paper presents an overview of investigations into the role of computer vision technology in developing safer automobiles. We consider vision systems which cannot 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.

关键词： Vehicle safety computer vision Vehicle driving Road vehicles Vehicle dynamics Automobiles Cameras Laboratories Machine vision Vehicle detection

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A track-based human movement analysis and privacy protection system adaptive to environmental contexts

A track-based human movement analysis and privacy protection...

引用

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

作者： Sangho Park M.M. Trivedi Computer Vision and Robotics Research (CVRR) Laboratory University of California San Diego La Jolla CA USA

This paper presents a track-based system for human movement analysis and privacy protection. Our system is adaptive to environmental contexts such as illumination variations, complex moving cast shadows, different camera perspectives, and diverse sue scenarios. Most of outdoor surveillance systems have been targeting at specific environmental situation: i.e., specific time, place, and activity scenarios. We address that more general human movement analysis systems should be able to handle multiple heterogeneous situations in an adaptive manner. We introduce the concept of "spatio-temporal personal boundary" to represent different grouping patterns of human tracks, and we incorporate the concept with various site models. Experimental evaluations with extensive outdoor data show our system's robustness to environmental changes and effectiveness to properly handle various environmental contexts.

关键词： Privacy Protection Adaptive systems Biomedical monitoring Humans Surveillance Layout Robot vision systems Target tracking Cameras

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Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis

Multiperspective Thermal IR and Video Arrays for 3D Body Tra...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

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

This paper presents a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video based system for robust and real-time 3D tracking of important body *** multi-perspective characteristics of the system provides 3Dtrajectory of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results demonstrate effectiveness of the proposed system.

关键词： Cameras computer vision Infrared detectors Humans Head Robustness Robot vision systems Image color analysis Real time systems Intelligent vehicles

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Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

Lane Change Intent Analysis Using Robust Operators and Spars...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： J.C. McCall M.M. Trivedi D. Wipf Bhaskar Rao Computer Vision and Robotics Research Laboratory University of California San Diego USA Digital Signal Processing Laboratory University of California San Diego USA

In this paper we demonstrate a driver intent inference system (DIIS) based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data collected in a modular intelligent vehicle test-bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.

关键词： Robustness Bayesian methods Vehicle driving computer vision Tracking Traffic control Inference algorithms Intelligent vehicles Testing Data analysis

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Robust real-time detection, tracking, and pose estimation of faces in video streams

Robust real-time detection, tracking, and pose estimation of...

引用

International Conference on Pattern Recognition

作者： K.S. Huang M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Robust human face analysis has been recognized as a crucial part in intelligent systems. In this paper, we present the development of a computational framework for robust detection, tracking, and pose estimation of faces captured by video arrays. We discuss the development of a multi-primitive skin-tone and edge-based detection module embedded in a tracking module for efficient and robust face detection and tracking. A continuous density HMM based pose estimation is developed for an accurate estimate of the face orientation motions. Experimental evaluations of these algorithms suggest the validity of the proposed framework and its computational modules.

关键词： Face detection Streaming media Face recognition Cameras Skin Humans Intelligent systems Noise robustness computer vision Intelligent robots

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Motion based vehicle surround analysis using an omni-directional camera

Motion based vehicle surround analysis using an omni-directi...

引用

IEEE Symposium on Intelligent Vehicle

作者： T. Gandhi M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Omni-directional cameras which give 360 degree panoramic view of the surroundings and have recently been used in many applications such as robotics, navigation and surveillance. This paper describes the application of motion estimation on omni camera to perform surround analysis using an automobile mounted camera. The system detects and tracks the surrounding vehicles by compensating the ego-motion and detecting objects having independent motion. Prior knowledge about ego-motion and calibration is optimally combined with the information from the image gradients to get better motion compensation.

关键词： Motion analysis Cameras Robot vision systems Object detection Motion detection Vehicle detection Navigation Surveillance Motion estimation Image motion analysis

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vision modules for a multi-sensory bridge monitoring approach

Vision modules for a multi-sensory bridge monitoring approac...

引用

International Conference on Intelligent Transportation

作者： R. Chang T. Gandhi M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

This work presents the use of multiple sensor modalities in order to perform traffic analysis for health monitoring of transportation infrastructure. In particular, testbeds containing video and seismic sensors giving complementary information about vehicles are described. computer vision algorithms are used to detect and track the vehicles and extract their properties. This information is combined with the data from seismic sensors for robust classification of vehicles. Experimental results obtained with our testbeds are described.

关键词： Bridges computerized monitoring Testing Performance analysis Transportation Sensor phenomena and characterization computer vision Vehicle detection Data mining Robustness

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