16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)最新文献_第8页

A multi-scale model integrating multiple features for vehicle detection 一种融合多种特征的车辆检测多尺度模型

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728264

Ye Li, Feiyue Wang, Bo Li, Bin Tian, F. Zhu, Gang Xiong, Kunfeng Wang

{"title":"A multi-scale model integrating multiple features for vehicle detection","authors":"Ye Li, Feiyue Wang, Bo Li, Bin Tian, F. Zhu, Gang Xiong, Kunfeng Wang","doi":"10.1109/ITSC.2013.6728264","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728264","url":null,"abstract":"In traffic video surveillance systems, vehicles with various distances from the camera have different sizes, resolutions, and angles in traffic images. The common multi-scale method, which scales one vehicle template or the input image for detecting vehicles with different sizes, may fail to detect vehicles with various distances from the camera due to the change of the resolution and angle. To deal with this problem, we have proposed a multi-scale model including multiple templates with different scales and features. Our method includes two steps: constructing the multi-scale model and its probability model, and detecting vehicles from traffic images. In the first step, the multi-scale model is constructed by using three templates T1, T2, T3 which represent vehicles with the short, medium, and long distance from the camera respectively. Each template contains one or some combination of sketch, texture, flatness, and color. In the second step, the three templates are applied for vehicle detection by using the template matching with local maximization operations. The main innovation of this paper is that the combination of multi-template and multi-scale method is applied to detect vehicles with various distances from the camera. To test our method, we have done several experiments on various traffic conditions. The experimental results show that our method effectively copes with vehicles with various distances from the camera and provides the detailed vehicle information after vehicle detection. Moreover, our method adapts to various weather conditions, slight pose variance, and slight occlusion.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"77 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121013776","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: 6

An intelligent carpooling app for a green social solution to traffic and parking congestions 一个智能拼车应用程序，为交通和停车拥堵提供绿色社会解决方案

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728586

Oussama Dakroub, Carl Michael Boukhater, Fayez Lahoud, M. Awad, H. Artail

{"title":"An intelligent carpooling app for a green social solution to traffic and parking congestions","authors":"Oussama Dakroub, Carl Michael Boukhater, Fayez Lahoud, M. Awad, H. Artail","doi":"10.1109/ITSC.2013.6728586","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728586","url":null,"abstract":"Because existing public transportation infrastructure cannot be adapted in a timely manner to address the daunting traffic and parking congestion in urban environments, researchers are investigating social solutions, such as carpooling, where a driver and one or more passengers having semi-common routes share a private vehicle. Although many carpooling systems have been proposed, most of them lack various levels of automation, functionality, practicality, and solution quality. While Genetic Algorithms (GAs) have been successfully adopted for solving combinatorial optimization problems, their use is highly uncommon in carpooling problems. Motivated to propose a solution for the many to many carpooling scenario, we present in this paper a GA with a customized fitness function that searches for the solution with minimal travel distance, efficient ride matching, timely arrival, and maximum fairness while taking into account the riding preferences of the carpoolers. The computational results and simulations based on real user data show the merits of the proposed method and motivate follow up research.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116631795","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: 16

A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles 自然驾驶风格行为聚类的两步分割算法

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728339

B. Higgs, M. Abbas

{"title":"A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles","authors":"B. Higgs, M. Abbas","doi":"10.1109/ITSC.2013.6728339","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728339","url":null,"abstract":"This research effort aims to investigate the hypothesis that drivers apply different driving styles in their daily driving tasks. A two-step algorithm is used for segmentation and clustering. First, a car-following period is broken into different duration segments that account for their temporal distribution. Second, the segments produced by the previous step are clustered based on similarity. Variations of k-means clustering and optimization techniques are used in this process. The segments centroids, used for clustering, are 8-dimensional and are produced by taking the average of the data points in each segment based on longitudinal acceleration, lateral acceleration, gyro (yaw rate), vehicle speed, lane offset, gamma (yaw angle), range, and range rate. The results of this methodology are continuous segments of car-following behavior as well as clusters of segments that show similar data and thus similar behaviors. The sample used in this paper included three different truck drivers that are representative of a high-risk driver, a medium-risk driver, and a low-risk driver. . In summary, the results revealed behavior that changed within a car-following period, between car-following periods, and between drivers. Each driver showed a unique distribution of behavior, but some of the behaviors existed in more than one driver but at different frequencies.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116635162","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: 37

Operating heavy duty vehicles under extreme heat conditions: A fuzzy approach for smart gear-shifting strategy 在极端高温条件下操作重型车辆:智能换挡策略的模糊方法

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728356

Stefanos Skalistis, D. Petrovic, S. Shaikh

引用次数: 1

Map matching accuracy: Energy efficient location sampling using smartphones 地图匹配精度:使用智能手机进行节能位置采样

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728561

M. Alrefaie, Iacopo Carreras, F. Cartolano, Roberto Di Cello, F. Rango

引用次数: 7

Two strategies for real time empty vehicle redistribution for the Personal Rapid Transit system 个人快速交通系统空车实时分配的两种策略

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728504

Ezzeddine Fatnassi, Olfa Chebbi, Jouhaina Chaouachi Siala

引用次数: 12

Sensors technologies for intelligent vehicles perception systems: A comparison between vision and 3D-LIDAR 智能车辆感知系统的传感器技术:视觉与3d激光雷达的比较

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728344

A. Broggi, P. Grisleri, P. Zani

引用次数: 22

High-efficient detection of traffic parameters by using two foreground temporal-spatial images 基于两幅前景时空图像的交通参数高效检测

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728517

Jianqiang Ren, Le Xin, Yangzhou Chen, Deliang Yang

{"title":"High-efficient detection of traffic parameters by using two foreground temporal-spatial images","authors":"Jianqiang Ren, Le Xin, Yangzhou Chen, Deliang Yang","doi":"10.1109/ITSC.2013.6728517","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728517","url":null,"abstract":"Real-time detection of vehicular volume, mean speed and vehicle type has important significance, but the existing video-based detection methods are not satisfactory at processing speed and accuracy. This paper proposes a high-efficient method to detect all the three parameters from two foreground temporal-spatial images (TSIs) directly, which are obtained from two virtual detection lines (VDLs) in video frames. Such usage of the TSIs provides a feasible approach to solve the problems of vehicle occlusion, mean-speed estimation, and vehicle classification without using original frame images. Firstly, for improving the accuracy of detection, during generation of the foreground TSIs, we set a small-wide region of interest for each VDL and propose a local background subtraction method and an improved moving shadows elimination method to eliminate unwanted interferences. Then, in order to reduce the calculation complexity, during extraction of the parameters, we analyze the feasibility of vehicle classification direct from the foreground TSIs, and propose a method to extract shape-feature vector from the TSIs directly. The dependence on original frame images is minimized, so the pressing speed is improved obviously. Experimental results prove the feasibility and efficiency of the proposed method.","PeriodicalId":275768,"journal":{"name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487135","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: 4

Observer-based tracking control of the vehicle lateral dynamics using four-wheel active steering 基于观测器的四轮主动转向车辆横向动力学跟踪控制

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728258

H. Dahmani, O. Pagès, A. Hajjaji, N. Daraoui

引用次数: 6

Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors 基于激光扫描仪轮廓的车辆类型分类:特征描述符的基准

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) Pub Date : 2013-10-01 DOI: 10.1109/ITSC.2013.6728283

Harsimrat Sandhawalia, José A. Rodríguez-Serrano, H. Poirier, G. Csurka

引用次数: 21