2013 IEEE Workshop on Robot Vision (WORV)最新文献_第3页

Ceiling analysis of pedestrian recognition pipeline for an autonomous car application 自动驾驶汽车行人识别管道的上限分析

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521941

H. Roncancio, A. C. Hernandes, M. Becker

引用次数: 6

Color-based detection robust to varying illumination spectrum 基于颜色的检测对不同的光照光谱具有鲁棒性

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521924

M. Linderoth, A. Robertsson, Rolf Johansson

引用次数: 6

Detecting partially occluded objects via segmentation and validation 通过分割和验证检测部分遮挡的物体

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521925

M. Levihn, M. Dutton, A. J. Trevor, M. Silman

引用次数: 2

Moving pedestrian detection based on motion segmentation 基于运动分割的运动行人检测

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521921

Shanshan Zhang, C. Bauckhage, D. A. Klein, A. Cremers

{"title":"Moving pedestrian detection based on motion segmentation","authors":"Shanshan Zhang, C. Bauckhage, D. A. Klein, A. Cremers","doi":"10.1109/WORV.2013.6521921","DOIUrl":"https://doi.org/10.1109/WORV.2013.6521921","url":null,"abstract":"The detection of moving pedestrians is of major importance in the area of robot vision, since information about such persons and their tracks should be incorporated into reliable collision avoidance algorithms. In this paper, we propose a new approach, based on motion analysis, to detect moving pedestrians. Our main contribution is to localize moving objects by motion segmentation on an optical flow field as a preprocessing step, so as to significantly reduce the number of detection windows needed to be evaluated by a subsequent people classifier, resulting in a fast method for real-time systems. Therefore, we align detection windows with segmented motion-blobs using a height-prior rule. Finally, we apply a Histogram of Oriented Gradients (HOG) features based Support Vector Machine with Radial Basis Function kernel (RBF-SVM) to estimate a confidence for each detection window, and thereby locate potential pedestrians inside the segmented blobs. Experimental results on “Daimler mono moving pedestrian detection” benchmark show that our approach obtains a log-average miss rate of 43% in the FPPI range [10-2, 100], which is a clear improvement with respect to the naive HOG+linSVM approach and better than several other state-of-the-art detectors. Moreover, our approach also reduces runtime per frame by an order of magnitude.","PeriodicalId":130461,"journal":{"name":"2013 IEEE Workshop on Robot Vision (WORV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129532131","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

Human-object-object-interaction affordance Human-object-object-interaction给养

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521912

Shaogang Ren, Yu Sun

引用次数: 28

Panorama creation using a team of robots 全景创建使用一队机器人

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521922

Yongqiang Huang, W. Snyder

引用次数: 0

Active object recognition using vocabulary trees 使用词汇树的主动对象识别

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521945

N. Govender, J. Claassens, F. Nicolls, J. Warrell

{"title":"Active object recognition using vocabulary trees","authors":"N. Govender, J. Claassens, F. Nicolls, J. Warrell","doi":"10.1109/WORV.2013.6521945","DOIUrl":"https://doi.org/10.1109/WORV.2013.6521945","url":null,"abstract":"For mobile robots to perform certain tasks in human environments, fast and accurate object classification is essential. Actively exploring objects by changing viewpoints promises an increase in the accuracy of object classification. This paper presents an efficient feature-based active vision system for the recognition and verification of objects that are occluded, appear in cluttered scenes and may be visually similar to other objects present. This system is designed using a selector-observer framework where the selector is responsible for the automatic selection of the next best viewpoint and a Bayesian `observer' updates the belief hypothesis and provides feedback. A new method for automatically selecting the `next best viewpoint' is presented using vocabulary trees. It is used to calculate a weighting for each feature based on its perceived uniqueness, allowing the system to select the viewpoint with the greatest number of `unique' features. The process is sped-up as new images are only captured at the `next best viewpoint' and processed when the belief hypothesis of an object is below some pre-defined threshold. The system also provides a certainty measure for the objects identity. This system out performs randomly selecting a viewpoint as it processes far fewer viewpoints to recognise and verify objects in a scene.","PeriodicalId":130461,"journal":{"name":"2013 IEEE Workshop on Robot Vision (WORV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132019649","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: 9

Design of microassembly system and research on coarse-to-fine alignment strategy in combination with active zooming 微装配系统设计及结合主动变焦的粗精对准策略研究

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521917

Zhengtao Zhang, Juan Zhang, De Xu

引用次数: 10

Incorporating local and global information using a novel distance function for scene recognition 结合局部和全局信息，使用新的距离函数进行场景识别

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521927

E. Farahzadeh, Tat-Jen Cham, W. Li

引用次数: 3

Monocular visual odometry from frame to frame intensity differences for planetary exploration mobile robots 行星探测移动机器人帧间强度差单目视觉测程

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521914

G. Martinez

引用次数: 7