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| Author | Jose Manuel Alvarez; Theo Gevers; Antonio Lopez | ||||
| Title | Evaluating Color Representation for Online Road Detection | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
| Volume | Issue | Pages | 594-595 | ||
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| Abstract | Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired using an on-board camera in different real-driving situations. |
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVVT:E2M | ||
| Notes | ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ AGL2013 | Serial | 2794 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
| Volume | Issue | Pages | 624-631 | ||
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| Abstract | Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVTT:E2M | ||
| Notes | IAM; ADAS; 600.044; 600.057; 601.145 | Approved | no | ||
| Call Number | Admin @ si @ MGH2013b | Serial | 2351 | ||
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| Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
| Title | Learning to count with deep object features | Type | Conference Article | ||
| Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 90-96 | ||
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| Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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| Address | Boston; USA; June 2015 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE International Conference on Computer Vision – Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2042-2049 | ||
| Keywords | IEEE International Conference on Computer Vision – Workshops | ||||
| Abstract | Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. | ||||
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| Publisher | IEEE | Place of Publication | Barcelona (Spain) | Editor | |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCVW | ||
| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ MGH2011 | Serial | 1682 | ||
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| Author | David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez | ||||
| Title | 2D-3D based on-board pedestrian detection system | Type | Journal Article | ||
| Year | 2010 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 114 | Issue | 5 | Pages | 583–595 |
| Keywords | Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms | ||||
| Abstract | During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system. | ||||
| Address | Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1077-3142 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSP2010 | Serial | 1341 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez | ||||
| Title | Stereo-based Candidate Generation for Pedestrian Protection Systems | Type | Book Chapter | ||
| Year | 2010 | Publication | Binocular Vision: Development, Depth Perception and Disorders | Abbreviated Journal | |
| Volume | Issue | 9 | Pages | 189–208 | |
| Keywords | Pedestrian Detection | ||||
| Abstract | This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. | ||||
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| Publisher | NOVA Publishers | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSL2010 | Serial | 1301 | ||
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| Author | David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf | ||||
| Title | Survey on Pedestrian Detection for Advanced Driver Assistance Systems | Type | Journal Article | ||
| Year | 2010 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 32 | Issue | 7 | Pages | 1239–1258 |
| Keywords | ADAS, pedestrian detection, on-board vision, survey | ||||
| Abstract | Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GLS2010 | Serial | 1340 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa | ||||
| Title | Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation | Type | Miscellaneous | ||
| Year | 2006 | Publication | 6th IASTED International Conference on Visualization, Imaging and Image Processing | Abbreviated Journal | VIIP |
| Volume | Issue | Pages | 400–405 | ||
| Keywords | ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms. | ||||
| Abstract | In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows. With this approach, the system can work in urban roads, as will be illustrated by current results. |
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| Address | Palma de Mallorca (Spain) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSL2006 | Serial | 672 | ||
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| Author | Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez | ||||
| Title | Efficient On-Board Stereo Vision Pose Estimation | Type | Conference Article | ||
| Year | 2007 | Publication | Computer Aided Systems Theory, Selected paper from | Abbreviated Journal | |
| Volume | 4739 | Issue | Pages | 1183–1190 | |
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| Abstract | This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented. |
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| Address | Las Palmas de Gran Canaria (Spain) | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | EUROCAST | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SDG2007b | Serial | 916 | ||
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| Author | Angel Sappa; Rosa Herrero; Fadi Dornaika; David Geronimo; Antonio Lopez | ||||
| Title | Road Approximation in Euclidean and v-Disparity Space: A Comparative Study | Type | Conference Article | ||
| Year | 2007 | Publication | Computer Aided Systems Theory, | Abbreviated Journal | |
| Volume | 4739 | Issue | Pages | 1105–1112 | |
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| Abstract | This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge. |
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| Address | Las Palmas de Gran Canaria (Spain) | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | EUROCAST | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SHD2007b | Serial | 917 | ||
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| Author | Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez | ||||
| Title | Real Time Vehicle Pose Using On-Board Stereo Vision System | Type | Conference Article | ||
| Year | 2006 | Publication | International Conference on Image Analysis and Recognition | Abbreviated Journal | ICIAR |
| Volume | Issue | LNCS 4142 | Pages | 205–216 | |
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| Abstract | This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented. |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SGD2006b | Serial | 671 | ||
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| Author | Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez | ||||
| Title | Registration-based Moving Object Detection from a Moving Camera | Type | Conference Article | ||
| Year | 2008 | Publication | IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles | Abbreviated Journal | |
| Volume | Issue | Pages | 65–69 | ||
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| Abstract | This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach. |
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| Address | Nice (France) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SDG2008 | Serial | 1017 | ||
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| Author | Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez | ||||
| Title | An Efficient Approach to Onboard Stereo Vision System Pose Estimation | Type | Journal Article | ||
| Year | 2008 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 9 | Issue | 3 | Pages | 476–490 |
| Keywords | Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system | ||||
| Abstract | This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SDP2008 | Serial | 1000 | ||
| Permanent link to this record | |||||
| Author | Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez | ||||
| Title | Stereo Vision Camera Pose Estimation for On-Board Applications | Type | Book Chapter | ||
| Year | 2007 | Publication | Scene Reconstruction, Pose Estimation and Traking | Abbreviated Journal | |
| Volume | Issue | Pages | 39-50 | ||
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| Publisher | Rustam Stolking | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-902613-06-6 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SGD2007 | Serial | 797 | ||
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| Author | Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez | ||||
| Title | On-board camera extrinsic parameter estimation | Type | Journal Article | ||
| Year | 2006 | Publication | Electronics Letters | Abbreviated Journal | EL |
| Volume | 42 | Issue | 13 | Pages | 745–746 |
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| Abstract | An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction. | ||||
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| Publisher | IEE | Place of Publication | Editor | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SGD2006a | Serial | 655 | ||
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