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检索条件"任意字段=27th Conference on Intelligent Robots and Computer Vision: Algorithms and Techniques"

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the 21th Annual intelligent Ground Vehicle Competition: Robotists for the Future

The 21TH Annual Intelligent Ground Vehicle Competition: Robo...

引用

conference on intelligent robots and computer vision XXXI - algorithms and techniques

作者： theisen, Bernard L. US Army TARDEC Warren MI 48397 USA

ISBN: (纸本)9780819499424

the intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). the IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 21 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. this paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. the primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. the employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

关键词： intelligent robots autonomous systems ground vehicles vehicle control engineering education IGVC.

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Seeing the Arrow of Time 27

Seeing the Arrow of Time

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27th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Pickup, Lyndsey C. Pan, Zheng Wei, Donglai Shih, YiChang Zhang, Changshui Zisserman, Andrew Schoelkopf, Bernhard Freeman, William T. Univ Oxford Dept Engn Sci Visual Geometry Grp Oxford OX1 2JD England Tsinghua Univ State Key Lab Intelligent Technol & Syst Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing Peoples R China MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781479951178

We explore whether we can observe Time's Arrow in a temporal sequence-is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.

关键词： Time forwards/backwards video

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Efficient Lane and Vehicle detection with Integrated Synergies (ELVIS)

Efficient Lane and Vehicle detection with Integrated Synergi...

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27th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Satzoda, Ravi Kumar Trivedi, Mohan M. Univ Calif San Diego Lab Intelligent & Safe Automobiles La Jolla CA 92093 USA

ISBN: (纸本)9781479943098

On-road vehicle detection and lane detection are integral parts of most advanced driver assistance systems (ADAS). In this paper, we introduce an integrated approach called Efficient Lane and Vehicle detection with Integrated Synergies (ELVIS), that exploits the inherent synergies between lane and on-road vehicle detection to improve the overall computational efficiency without compromising on the robustness of both the tasks. Detailed evaluations show that the vehicle detection component of ELVIS shows at least 50% lesser false alarms with equal or better detection rates, and reducing the computational costs by over 90% as compared to state-of-the-art vehicle detection methods. Similarly, the lane detection component shows more reliable lane feature extraction with average computation costs that are at least 35% lesser than existing techniques.

关键词： vehicle detection lane detection integrated system computational efficiency

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3D Modeling from Wide Baseline Range Scans using Contour Coherence 27

3D Modeling from Wide Baseline Range Scans using Contour Coh...

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27th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Wang, Ruizhe Choi, Jongmoo Medioni, Gerard Univ So Calif Inst Robot & Intelligent Syst Comp Vis Lab Los Angeles CA 90089 USA

ISBN: (纸本)9781479951178

Registering 2 or more range scans is a fundamental problem, with application to 3D modeling. While this problem is well addressed by existing techniques such as ICP when the views overlap significantly at a good initialization, no satisfactory solution exists for wide baseline registration. We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization. Inspired by ICP, we maximize the contour coherence by building robust corresponding pairs on apparent contours and minimizing their distances in an iterative fashion. We use the contour coherence under a multi-view rigid registration framework, and this enables the reconstruction of accurate and complete 3D models from as few as 4 frames. We further extend it to handle articulations, and this allows us to model articulated objects such as human body. Experimental results on both synthetic and real data demonstrate the effectiveness and robustness of our contour coherence based registration approach to wide baseline range scans, and to 3D modeling.

关键词： Iterative methods

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Packing and Padding: Coupled Multi-index for Accurate Image Retrieval 27

Packing and Padding: Coupled Multi-index for Accurate Image ...

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27th IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Zheng, Liang Wang, Shengjin Liu, Ziqiong Tian, Qi Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781479951178

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. the joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

关键词： Image retrieval

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the 20th Annual intelligent Ground Vehicle Competition: Building a Generation of Robotists

The 20TH Annual Intelligent Ground Vehicle Competition: Buil...

引用

conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： theisen, Bernard L. Kosinski, Andrew US Army TARDEC Warren MI 48397 USA

ISBN: (纸本)9780819494351

the intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). the IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 20 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. this paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. the primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. the employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

关键词： intelligent robots autonomous systems ground vehicles vehicle control engineering education IGVC

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Designing and Making the Intelligence Assistant Robot and controlling it by the fuzzy procedure

Designing and Making the Intelligence Assistant Robot and co...

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10th International conference on Electronics, computer and Computation (ICECCO)

作者： Zabbah, Iman Foolad, Shima Chaharaqran, Bahram Mazlooman, Reza Islamic Azad Univ Torbat E Heydarieh Branch Dept Comp Tehran Iran Semnan Univ Dept Comp Semnan Iran Maraghen Sanati Univ Dept Comp Maragheh Iran Natl Iranian Oil Prod Distribut Co Torbat Heydarieh Branch Torbat Heydarieh Iran

ISBN: (纸本)9781479933433

Mobile robots are those with the capability of moving in an environment. the controller of these robots needs to have the ability to do intelligent behaviours in an uncertain environment. Today fuzzy control of mobile robots in non-structured environments is increasing. Many represented algorithms for mobile robot navigation don't manifest appropriate performance in dynamic environments. this article aims at designing and making an intelligence robot. this robot is able to detect a specified target and track it. Velocity and steering of this robot is controlled by the fuzzy logic. Robot velocity is adjusted according to its distance from the target. this article is expressed the features of this mobile robot including different mechanical and electronic infrastructures, fuzzy navigation and vision controller. Navigation system of this robot makes its fuzzy decisions only based on the machine vision techniques and unlike many robots, it doesn't depend on sensor. It is concluded that robot with fuzzy navigation have 60% time saving than the non-fuzzy. the results obtained indicate its appropriate performance in order to navigate a target-oriented mobile robot in an undetermined and dynamic environment.

关键词： Mobile robot navigation Machine vision Robot fuzzy control Target tracking Intelligence robot

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Design and Realization of an intelligent Ground Vehicle with Modular Payloads

Design and Realization of an Intelligent Ground Vehicle with...

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conference on intelligent robots and computer vision XXIX - algorithms and techniques

作者： Akmanalp, Mehmet A. Doherty, Ryan M. Gorges, Jeffrey Kalauskas, Peter Peterson, Ellen Polido, Felipe Nestinger, Stephen S. Padir, Taskin Worcester Polytech Inst Robot Engn Program Worcester MA 01609 USA

ISBN: (纸本)9780819489487

In June 2011, Worcester Polytechnic Institute's (WPI) unmanned ground vehicle Prometheus participated in the 8th Annual Robotic Lawnmower and 19th Annual intelligent Ground Vehicle Competitions back-to-back. this paper details the two-year design and development cycle for WPI's intelligent ground vehicle, Prometheus. the on-board intelligence algorithms include lane detection, obstacle avoidance, path planning, world representation and waypoint navigation. the authors present experimental results and discuss practical implementations of the intelligence algorithms used on the robot.

关键词： Autonomous ground vehicle modular payloads path planning

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the 19th Annual intelligent Ground Vehicle Competition: Student Built Autonomous Ground Vehicles

The 19TH Annual Intelligent Ground Vehicle Competition: Stud...

引用

conference on intelligent robots and computer vision XXIX - algorithms and techniques

作者： theisen, Bernard L. USA TARDEC Warren MI 48397 USA

ISBN: (纸本)9780819489487

the intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). the IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 19 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from almost 80 universities and colleges have participated. this paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. the primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. the employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

关键词： intelligent robots autonomous systems ground vehicles vehicle control engineering education IGVC

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Real-time background modeling based on classified dynamic objects for human robot application

Real-time background modeling based on classified dynamic ob...

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9th International conference on Networked Sensing Systems, INSS 2012

作者： Moro, Alessandro Mumolo, Enzo Nolich, Massimiliano Terabayashi, Kenji Umeda, Kazunori Department of Precision Mechanics Faculty of Science and Engineering Chuo University/CREST 1-13-27 Kasuga Bunkyo-ku Tokyo 112-8551 Japan DI3 University of Trieste Via Valerio 10 34123 Trieste Italy IFACE s.r.l. Via Valerio 10 34123 Trieste Italy

ISBN: (纸本)9781467317856

the aim of this paper is to describe a flexible and robust background management algorithm. In these years various techniques were proposed to segment the images from a video stream sequence, and detect interesting dynamic objects. Many works faced the problem to segment the image in indoor environment for human detection and intelligent room applications. In these works, both accuracy and efficiency depend on the background model they used. Specific high performances models suffer of some limitations in chaotic unstructured environments. Long video stream sequences changes in light condition, and object and human displacement. In those environments, dynamic to stable objects and humans can be absorbed in the background, and then become invisible to the system. In this work we propose an approach to combine low level and high level information to improve the background management and to solve unpredictable object dynamic problems. Experimental recall and precision results show improved performances with respect to popular background management algorithms. Finally, a real application is shown and discussed. © 2012 IEEE.

关键词： computer vision

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