2011 IEEE Intelligent Vehicles Symposium (IV)最新文献_第9页

Towards rain detection through use of in-vehicle multipurpose cameras 利用车载多用途摄影机探测雨水

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940484

A. Cord, D. Aubert

引用次数: 24

Pedestrian detectability: Predicting human perception performance with machine vision 行人可探测性:用机器视觉预测人类感知性能

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940445

David Engel, Cristóbal Curio

引用次数: 13

Autonomous exploration of unknown terrain for groups of mobile robots 移动机器人群对未知地形的自主探索

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940455

Samuel Lopes, Brian Frisch, A. Boeing, K. Vinsen, T. Bräunl

引用次数: 5

Large scale sign detection using HOG feature variants 基于HOG特征变体的大规模标志检测

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940549

G. Overett, L. Petersson

{"title":"Large scale sign detection using HOG feature variants","authors":"G. Overett, L. Petersson","doi":"10.1109/IVS.2011.5940549","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940549","url":null,"abstract":"In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132839430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 80

Generic grid mapping for road course estimation 用于道路航向估计的通用网格映射

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940514

M. Konrad, Magdalena Szczot, F. Schüle, K. Dietmayer

引用次数: 31

Development and evaluation of a scheme for detecting multiple approaching vehicles through acoustic sensing 基于声传感的多车辆逼近检测方案的开发与评价

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940423

K. Asahi, Hideki Banno, Osami Yamamoto, A. Ogawa, K. Yamada

引用次数: 5

Common traffic congestion features studied in USA, UK, and Germany based on kerner's three-phase traffic theory 基于kerner的三阶段交通理论，研究了美国、英国和德国的常见交通拥堵特征

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940394

H. Rehborn, S. Klenov, J. Palmer

引用次数: 14

Eye detection for a real-time vehicle driver fatigue monitoring system 一种用于车辆驾驶员疲劳实时监测的眼睛检测系统

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940406

R. Coetzer, G. Hancke

{"title":"Eye detection for a real-time vehicle driver fatigue monitoring system","authors":"R. Coetzer, G. Hancke","doi":"10.1109/IVS.2011.5940406","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940406","url":null,"abstract":"With the vast amount of vehicles on roads worldwide on any given time of day, the severity of fatigue related accidents have become a major concern. The obvious solution to prevent or at least decrease fatigue related accidents, is to ensure that the driver rests frequently. However, the simple fact of the matter is that frequent resting periods cannot be effectively enforced, and as a result there is a need for a system to monitor the level of driver fatigue in real-time. The ultimate goal of this research is to develop a camera-based driver fatigue monitoring system, centered around the tracking of driver's eyes, since the eyes provide the most information with regards to fatigue. The most critical aspect of eye tracking is to first accurately detect the eyes, and although a number of eye trackers have already been illustrated in the literature, the process of eye detection has seldom been described in much detail. Given a number of possible eye candidate sub-images, eye detection is in essence the classification of these sub-images as either eyes or non-eyes. To the knowledge of the authors, different classification techniques have not been directly compared for the purpose of eye detection, and therefore the aim of this paper is to evaluate different classification techniques to determine which technique will be the most suitable for a driver fatigue monitoring system. The classification techniques that have been considered are artificial neural networks (ANN), support vector machines (SVM) and adaptive boosting (AdaBoost). Results have shown that AdaBoost will be the most suitable eye classification technique for a real-world driver fatigue monitoring system.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 67

Dynamic motion planning for autonomous vehicle in unknown environments 未知环境下自动驾驶汽车的动态运动规划

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940506

Pan Zhao, Jiajia Chen, Tao Mei, Huawei Liang

引用次数: 51

Novel two-stage algorithm for non-parametric cast shadow recognition 一种新的两阶段非参数投影识别算法

2011 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2011-06-05 DOI: 10.1109/IVS.2011.5940560

Martin Roser, Philip Lenz

引用次数: 0