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检索条件"机构=Computer Vision and Robotics Research"

共 377 条记录，以下是161-170 订阅

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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

Pedestrian detection based on LIDAR-driven sliding window an...

引用

IEEE Symposium on Intelligent Vehicle

作者： Luciano Oliveira Urbano Nunes Intelligent Vision Research Laboratory Federal University Bahia Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.

关键词： Detectors Laser radar Feature extraction Support vector machines Training Probabilistic logic Vectors

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Radiometric calibration by transform invariant low-rank structure

Radiometric calibration by transform invariant low-rank stru...

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作者： Lee, Joon-Young Shi, Boxin Matsushita, Yasuyuki Kweon, In-So Ikeuchi, Katsushi Robotics and Computer Vision Lab KAIST Korea Republic of University of Tokyo Japan Microsoft Research Asia China

ISBN: (纸本)9781457703942

We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem;therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches. © 2011 IEEE.

关键词： Radiometry

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Learning multi-lane trajectories using vehicle-based vision

Learning multi-lane trajectories using vehicle-based vision

引用

International Conference on computer vision Workshops (ICCV Workshops)

作者： Sayanan Sivaraman Brendan Morris Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, this work proposes a framework for understanding the driving situation based on vehicle mounted vision sensors. Vehicles are tracked using Kalman filtering based on a vision-based system that detects and tracks using a combination of monocular and stereo-vision. The vehicles' full trajectories are recorded, and a data-driven learning framework has been applied to automatically learn surround behaviors. By learning based on observations, the ADAS system is being trained by experience. Learned trajectories have been compared between dense and free-flowing traffic conditions. Preliminary experimental results using real-world multi-lane highways show the basic promise of this approach. Future research directions are discussed.

关键词： Vehicles Trajectory Hidden Markov models Mathematical model Detectors Equations Training

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Radiometric Calibration by Transform Invariant Low-rank Structure

Radiometric Calibration by Transform Invariant Low-rank Stru...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Joon-Young Lee Boxin Shi Yasuyuki Matsushita In-So Kweon Katsushi Ikeuchi Robotics and Computer Vision Lab KAIST The University of Tokyo Microsoft Research Asia

ISBN: (纸本)9781457703942

关键词： exposure time incoming data Radiation Response function Invariant Transform CALIBRATION METHODS

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Retinal vessel tortuosity evaluation via circular hough transform

Retinal vessel tortuosity evaluation via circular hough tran...

引用

2011 18th Iranian Conference of Biomedical Engineering, ICBME 2011

作者： Ghadiri, Farnoosh Pourreza, Hamidreza Banaee, Touka Delgir, Morteza Computer Engineering Department Machine Vision Lab Ferdowsi University Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical Science University Mashhad Iran Computer Science Department Robotics Lab Northeastern University United States

ISBN: (纸本)9781467310055

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods. © 2011 IEEE.

关键词： Database systems

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Lidar based off-road negative obstacle detection and analysis

Lidar based off-road negative obstacle detection and analysi...

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International Conference on Intelligent Transportation

作者： Jacoby Larson Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in realtime: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found off-road are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (SVM) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

关键词： Laser radar Vehicles Support vector machines Robot sensing systems Lasers Three dimensional displays Equations

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Boosting based object detection using a geometric model

Boosting based object detection using a geometric model

引用

IEEE International Conference on Image Processing

作者： Katharina Quast Christoph Seeger Mohan Trivedi André Kaup Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Computer Vision and Robotics Research University of California San Diego La Jolla CA USA

In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular Ad aBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data.

关键词： Detectors Feature extraction Vectors Boosting Training Kernel Object detection

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An accurate 3D feature tracking system with wide-baseline stereo smart cameras

An accurate 3D feature tracking system with wide-baseline st...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Dominik Rueß Kristian Manthey Ralf Reulke German AeroSpace Center Optical Information Systems Institute of Robotics and Mechatronics Berlin Germany Department of Computer Science Research and Lecture Group Computer Vision Humboldt-Universität zu Berlin Berlin Germany

A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.

关键词： Trajectory Three dimensional displays Cameras Smart cameras Estimation Image reconstruction Feature extraction

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Retinal vessel tortuosity evaluation via Circular Hough Tran...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Farnoosh Ghadiri HamidReza Pourreza Touka Banaee Morteza Delgir Computer Engineering Department Machine Vision Laboratory Ferdowsi University of Mashhad Iran Computer Engineering Department Machine Vision Laboratory University of Ferdowsi Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical science University of Mashhad Iran Computer Science Department Robotics Laboratory Northeastern University USA

关键词： Transforms Retinal vessels Classification algorithms Biomedical measurements Biomedical imaging Image segmentation

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Contextual activity visualization from long-term video observations

Contextual activity visualization from long-term video obser...

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作者： Morris, Brendan Tran Trivedi, Mohan Manubhai Computer Vision and Robotics Research Laboratory University of California San Diego United States

The Contextual Activity Notification Visualization Analysis System (Canvas) provides a user interaction interface for instantaneous feedback of contextual processing units that enable high-level semantic extraction an... 详细信息

关键词： Visualization

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