Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)最新文献_第8页

A new method of railway passenger flow forecasting based on spatio-temporal data mining 基于时空数据挖掘的铁路客流预测新方法

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398932

Wei Xu, Yong Qin, Houkuan Huang

引用次数: 7

A reliable road lane detector approach combining two vision-based algorithms 一种结合两种视觉算法的可靠道路车道检测方法

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398888

R. Labayrade, S. Leng, D. Aubert

引用次数: 19

Real-time multiple face detection of pedestrian using hybrid GA 基于混合遗传算法的行人实时多人脸检测

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398988

H. Suzuki, M. Minami

引用次数: 3

Evaluation performance of the adaptive cell scheme in road to vehicle communications 自适应小区方案在路车通信中的性能评价

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399008

K. Nishino, T. Hasegawa

引用次数: 0

On the intermittence of routing paths in vehicle-formed mobile ad hoc networks on highways 高速公路车载移动自组网中路由路径的间歇性研究

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399005

S.Y. Wang

引用次数: 23

Motion stream analysis based on perceptual feature partitioning and grouping 基于感知特征划分与分组的运动流分析

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398964

Q. Gao, Y. Zhang, A. Parslow

{"title":"Motion stream analysis based on perceptual feature partitioning and grouping","authors":"Q. Gao, Y. Zhang, A. Parslow","doi":"10.1109/ITSC.2004.1398964","DOIUrl":"https://doi.org/10.1109/ITSC.2004.1398964","url":null,"abstract":"We present a perceptual organization based on method for motion stream analysis. The computation model was developed based upon a perception principle: visual feature partitioning and grouping. In the method, perceptual edge features are extracted and classified into generic edge tokens (GETs) using edge tracking and partitioning on the fly. GETs are perceptually distinctive features of lines and curve segments. Various structures and patterns of GETs can be grouped in terms of the rules of perceptual organization laws. GETs are descriptive and therefore can be manipulated qualitatively. For each consecutive image pair, motion GETs (MGETs) are segmented by directly subtracting the GETs extracted in the first image from the same locations in the second image, in that no explicit GET pair matching is needed. The MGETs are then grouped into clusters based on selected rules and domain knowledge of the objects. The motion clusters are evaluated using the measure of motion persistence (over multi-frames) for eliminating unstable data, i.e. noises. Two result demonstrations include road mark following and vehicle tracking.","PeriodicalId":239269,"journal":{"name":"Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129548624","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: 2

Adaptive image processing and sensing technologies for detecting anomalous conditions near critical transportation infrastructure assets 用于检测关键交通基础设施资产附近异常情况的自适应图像处理和传感技术

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398915

M. Magee, M. Rigney

{"title":"Adaptive image processing and sensing technologies for detecting anomalous conditions near critical transportation infrastructure assets","authors":"M. Magee, M. Rigney","doi":"10.1109/ITSC.2004.1398915","DOIUrl":"https://doi.org/10.1109/ITSC.2004.1398915","url":null,"abstract":"This work presents a methodology for detecting anomalous conditions on transportation infrastructure assets and generating alarms to draw the attention of personnel monitoring them via various sensing technologies. The methodology developed, which consists of a four part process, is capable of detecting conditions that vary substantially from those that are normally observed. This four part process consists of the following steps: (1) an intensity characteristic model of the transportation asset is learned during a training phase. (2) During the foreground object segmentation phase, objects are segmented based on pixel characteristics that vary substantially from those embodied in the intensity characteristic model. (3) Segmented objects that match certain morphological, topological, and/or geometric constraints are flagged as being candidates for further (temporal) processing. (4) Segmented objects with unanticipated temporal persistence or geometric characteristics are then identified and brought to the attention of a human operator.","PeriodicalId":239269,"journal":{"name":"Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132334348","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: 0

Real-time detection of crossing pedestrians for traffic-adaptive signal control 交通自适应信号控制中交叉口行人的实时检测

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398916

V. Bhuvaneshwar, P. Mirchandani

引用次数: 7

Time delay compensation for networked control systems based on SMC 基于SMC的网络控制系统时延补偿

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399010

Qian Wang, J. Yi, Dongbin Zhao, Bing Wu

引用次数: 4

An automatic inspection system for the hexagonal headed bolts detection in railway maintenance 一种铁路维修中六角头螺栓自动检测系统

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398936

P. Mazzeo, M. Nitti, E. Stella, N. Ancona, A. Distante

{"title":"An automatic inspection system for the hexagonal headed bolts detection in railway maintenance","authors":"P. Mazzeo, M. Nitti, E. Stella, N. Ancona, A. Distante","doi":"10.1109/ITSC.2004.1398936","DOIUrl":"https://doi.org/10.1109/ITSC.2004.1398936","url":null,"abstract":"Rail inspection is a very important task in railway maintenance for traffic safety and for preventing dangerous situations. Railway infrastructure monitoring is a particular application context in which the periodical inspection of rail rolling plane is required. Usually the inspection of the rail is operated manually. A trained human operator walks along the track, searching for visual anomalies. Actually, the described monitoring ways are not more acceptable for their slowness and for the lack of objectivity. In fact, the results are constrained to the ability of the observer to recognize critical situations. This paper presents a vision-based technique to detect, automatically, the presence or absence of the fastening bolts that fix the rails to the sleepers. The inspection system acquires images by a digital line scan camera installed under a train. The images are pre-processed by using wavelet transform with Haar and Daubechies approximation coefficients. We have used two pre-processing techniques in order to reduce the computational time and speed up the whole fastening bolt detecting process. These coefficients are supplied as input to two different neural networks, in this way the first neural network identify the fastening bolt candidates and the second neural network validates the recognition process of the bolt. The final detecting system has been applied to a long sequence of real images showing a high reliability, robustness and good performance in term of computational speed.","PeriodicalId":239269,"journal":{"name":"Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116453391","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: 8