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

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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...

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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...

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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...

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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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Enhancement for face video from omni-directional video camera

Enhancement for face video from omni-directional video camer...

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Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and computers

作者： Wu, Junwen Trivedi, Mohan M. Computer Vision and Robotics Research Lab ECE Department UC San Diego San Diego CA 92093 United States

In this paper we propose a novel algorithm to enhance the face video from omni-directional video camera. A two-stage strategy is used. First stage is the noise elimination, realized by iterative MAP update. Naive Bayesian criterion is used to model the posterior. The predominant salt and pepper noise introduced by the omni-to-perspective transformation can be removed effectively, while the image details are well-preserved. The high frequency component compensation (HFCC) super-resolution algorithm is applied thereafter to remove the blocky effect. Experimental results show that the video quality has a marked improvement. © 2004 IEEE.

关键词： Image enhancement

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Motion based vehicle surround analysis using an omni-directional camera

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

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

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

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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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Human posture estimation using voxel data for "smart" airbag systems: issues and framework

Human posture estimation using voxel data for "smart" airbag...

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IEEE Symposium on Intelligent Vehicle

作者： S.Y. Cheng M.M. Trivedi Computer Vision and Robotics Research University of California San Diego CA USA

This paper examines the feasibility of a multi-camera voxel based occupant posture estimation system. Several new considerations are made to allow this tested human body modeling system to work reliably in the passenger seat of a vehicle, including camera position, segmentation, and body modeling with voxel reconstructions all from a constrained 4 camera setup. To describe occupant posture, a partial human body model consisting of a head and torso is proposed. The accuracy of the estimation of this model is compared against ground-truth.

关键词： Humans Cameras Biological system modeling Head Image reconstruction Vehicles Torso Calibration computer vision Robot vision systems

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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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