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Vehicle mounted wide fov stereo for traffic and pedestrian detection

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

引用

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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Robust facial landmark detection for intelligent vehicle system

Robust facial landmark detection for intelligent vehicle sys...

引用

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

This paper presents an integrated approach for robustly locating facial landmark for drivers. In the first step a cascade of probability learners is used to detect the face edge primitives from fine to coarse, so that faces with variant head poses can be located. The edge density descriptors and skin-tone color features are combined together as the basic features to examine the probability of an edge being a face primitive. A cascade of the probability learner is used. In each scale, only edges with sufficient large probabilities are kept and passed on to the next scale. The final output of the cascade gives the edge primitives that belong to faces, which determine the face location. In the second step, a facial landmark detection procedure is applied on the segmented face pixels. Facial landmark candidates are first detected by learning the posteriors in multiple resolutions. Then geometric constraint and the local appearance, modeled by SIFT descriptor, are used to find the set of facial landmarks with largest matching score. Experiments over high-resolution images (FERET database) as well as the real-world drivers' data are used to evaluate the performance. A fairly good results can be obtained, which validates the proposed approach. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Face recognition

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Dynamic panoramic surround map: Motivation and omni video based approach

Dynamic panoramic surround map: Motivation and omni video ba...

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

作者： Gandhi, Tarak Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego San DiegoCA92093 United States

ISBN: (纸本)0769526608

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. © 2005 IEEE computer Society. All rights reserved.

关键词： Object detection

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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

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

作者： Cheng, Shinko Y. Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA United States

ISBN: (纸本)0769526608

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. © 2005 IEEE computer Society. All rights reserved.

关键词： Automobile drivers

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Driver activity analysis for intelligent vehicles: issues and development framework

Driver activity analysis for intelligent vehicles: issues an...

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IEEE Symposium on Intelligent Vehicle

作者： S. Park Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

This paper examines the feasibility of a semantic-level driver activity analysis system. Several new considerations are made to construct the hierarchy of driver activity. Driver activity is represented and recognized at multiple levels: individual body-part pose/gesture at the low level, single body-part action at the middle level, and the driver interaction with the vehicle at the high level. Driving is represented in terms of the interactions among driver, vehicle, and surround, and driver activity is recognized by a rule-based decision tree. Our system works with a single color camera data, and it can be easily expanded to incorporate multimodal sensor data.

关键词： Intelligent vehicles Vehicle driving Vehicle safety computer vision Robot vision systems Global Positioning System Vehicle detection Roads Torso Head

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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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IEEE Symposium on Intelligent Vehicle

作者： J.C. McCall M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

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.

关键词： Roads Monitoring Vehicle driving Navigation Intelligent robots Intelligent vehicles Humans Alarm systems Layout computer vision

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3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array

3D Shape Context Based Gesture Analysis Integrated with Trac...

引用

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

作者： Kohsia S. Huang M.M. Trivedi Computer Vision and Robotics Research (CVRR) Laboratory University of California San Diego USA

In this paper we introduce a multilayer cylindrical histogram based feature space for representing 3D shape context of human body. Dynamics of gestures are analyzed using discrete hidden Markov models (DHMM) with quantized feature vectors. Extensive experimental trials with multiple subjects and a range of gesture classes are presented. Gesture recognition accuracies of over 85% (for nine gestures, and 9 subjects) and over 95% (for seven gestures) support the basic feasibility and robustness of the approach.

关键词： Shape Humans Hidden Markov models Biological system modeling Cameras Robustness Context modeling Information analysis Pattern analysis computer vision

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Vehicle mounted wide FOV stereo for traffic and pedestrian detection

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

引用

IEEE International Conference on Image Processing

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

关键词： Vehicle detection Cameras Roads Stereo vision Object detection computer vision Calibration Motion analysis Robot vision systems Intelligent vehicles

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computer vision for homeland security: a perspective on its promise and pitfalls

Computer vision for homeland security: a perspective on its ...

引用

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

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

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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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