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Author | Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Xavier Carrillo; R. Hemetsberger; Petia Radeva; J. Mauri; A. Bayes | ||||
Title | Canvis de volum a la arteria radial despres de la administracio de dos tractaments vasodilatadors. Avaluacio mitjançant ecografia intravascular | Type | Conference Article | ||
Year | 2010 | Publication | 22nd Congres Societat Catalana de Cardiologia, | Abbreviated Journal | |
Volume | Issue | Pages | 179 | ||
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Address | Barcelona (Spain) | ||||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ SFC2010a | Serial | 1367 | ||
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Author | Oriol Rodriguez-Leor; R. Hemetsberger; Francesco Ciompi; E Fernandez-Nofrerias; Angel Serrano; M. Bernet; Petia Radeva; J. Mauri; A. Bayes | ||||
Title | Caracteritzacio automatica de la placa mitjançant analisis del espectre de radiofreqüencia en estudi de ecografia intracoronaria: resultat de la fusio de dades invivo i exvivo | Type | Conference Article | ||
Year | 2010 | Publication | 22nd Congres Societat Catalana de Cardiologia, | Abbreviated Journal | |
Volume | Issue | Pages | 131 | ||
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Address | Barcelona (Spain) | ||||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ RHC2010 | Serial | 1368 | ||
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Author | R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. | ||||
Title | On-line Semantic Perception Using Uncertainty | Type | Conference Article | ||
Year | 2012 | Publication | International Conference on Intelligent Robots and Systems | Abbreviated Journal | IROS |
Volume | Issue | Pages | 4185-4191 | ||
Keywords | Semantic Segmentation | ||||
Abstract | Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance | ||||
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Area | Expedition | Conference | IROS | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ NRR2012 | Serial | 2378 | ||
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Author | Daniel Ponsa; Joan Serrat; Antonio Lopez | ||||
Title | On-board image-based vehicle detection and tracking | Type | Journal Article | ||
Year | 2011 | Publication | Transactions of the Institute of Measurement and Control | Abbreviated Journal | TIM |
Volume | 33 | Issue | 7 | Pages | 783-805 |
Keywords | vehicle detection | ||||
Abstract | In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ PSL2011 | Serial | 1413 | ||
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Author | David Geronimo; Antonio Lopez | ||||
Title | Deteccion de Peatones para Sistemas Avanzados de Asistencia al Conductor | Type | Miscellaneous | ||
Year | 2010 | Publication | UAB Divulga | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Los sistemas de asistencia al conductor, y particularmente los sistemas de protección de peatones, representan uno de los campos de investigación más activos dedicados a la mejora de la seguridad vial. El mayor desafío es el desarrollo de sistemas a bordo fiables de detección de peatones. En esta revisión del estado de la técnica de la detección de peatones, se divide el problema en diferentes etapas, cada una con responsabilidades propias dentro del sistema. Esta división facilita el posterior análisis y discusión de cada uno de los métodos en la literatura, favoreciendo la comparación entre ellos. Finalmente se discuten los temas más importantes de este campo poniendo especial énfasis en las necesidades actuales y los desafíos futuros. | ||||
Address | Bellaterra (Catalonia), Spain | ||||
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Notes | spreading;ADAS | Approved | no | ||
Call Number | ADAS @ adas @ GeL2010a | Serial | 1414 | ||
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Author | David Augusto Rojas; Fahad Shahbaz Khan; Joost Van de Weijer | ||||
Title | The Impact of Color on Bag-of-Words based Object Recognition | Type | Conference Article | ||
Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1549–1553 | ||
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Abstract | In recent years several works have aimed at exploiting color information in order to improve the bag-of-words based image representation. There are two stages in which color information can be applied in the bag-of-words framework. Firstly, feature detection can be improved by choosing highly informative color-based regions. Secondly, feature description, typically focusing on shape, can be improved with a color description of the local patches. Although both approaches have been shown to improve results the combined merits have not yet been analyzed. Therefore, in this paper we investigate the combined contribution of color to both the feature detection and extraction stages. Experiments performed on two challenging data sets, namely Flower and Pascal VOC 2009; clearly demonstrate that incorporating color in both feature detection and extraction significantly improves the overall performance. | ||||
Address | Istanbul (Turkey) | ||||
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ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
Area | Expedition | Conference | ICPR | ||
Notes | Approved | no | |||
Call Number | CAT @ cat @ RKW2010 | Serial | 1415 | ||
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Author | Jaume Gibert; Ernest Valveny | ||||
Title | Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. | Type | Conference Article | ||
Year | 2010 | Publication | 13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition | Abbreviated Journal | |
Volume | 6218 | Issue | Pages | 223–232 | |
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Abstract | Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano, | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-14979-5 | Medium | |
Area | Expedition | Conference | S+SSPR | ||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ GiV2010 | Serial | 1416 | ||
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Author | David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
Title | Real-time Object Segmentation using a Bag of Features Approach | Type | Conference Article | ||
Year | 2010 | Publication | 13th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | 220 | Issue | Pages | 321–329 | |
Keywords | Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors | ||||
Abstract | In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. | ||||
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Publisher | IOS Press Amsterdam, | Place of Publication | Editor | In R.Alquezar, A.Moreno, J.Aguilar. | |
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ISSN | ISBN | 9781607506423 | Medium | ||
Area | Expedition | Conference | CCIA | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ ARL2010b | Serial | 1417 | ||
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Author | Murad Al Haj; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca | ||||
Title | Reactive object tracking with a single PTZ camera | Type | Conference Article | ||
Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1690–1693 | ||
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Abstract | In this paper we describe a novel approach to reactive tracking of moving targets with a pan-tilt-zoom camera. The approach uses an extended Kalman filter to jointly track the object position in the real world, its velocity in 3D and the camera intrinsics, in addition to the rate of change of these parameters. The filter outputs are used as inputs to PID controllers which continuously adjust the camera motion in order to reactively track the object at a constant image velocity while simultaneously maintaining a desirable target scale in the image plane. We provide experimental results on simulated and real tracking sequences to show how our tracker is able to accurately estimate both 3D object position and camera intrinsics with very high precision over a wide range of focal lengths. | ||||
Address | Istanbul (Turkey) | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
Area | Expedition | Conference | ICPR | ||
Notes | ISE | Approved | no | ||
Call Number | DAG @ dag @ ABG2010 | Serial | 1418 | ||
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Author | Anjan Dutta; Umapada Pal; Alicia Fornes; Josep Llados | ||||
Title | An Efficient Staff Removal Technique from Printed Musical Documents | Type | Conference Article | ||
Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1965–1968 | ||
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Abstract | Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results. | ||||
Address | Istanbul (Turkey) | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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Series Volume | Series Issue | Edition | |||
ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
Area | Expedition | Conference | ICPR | ||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ DPF2010 | Serial | 1420 | ||
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Author | Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny | ||||
Title | Symbol Classification using Dynamic Aligned Shape Descriptor | Type | Conference Article | ||
Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1957–1960 | ||
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Abstract | Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. | ||||
Address | Istanbul (Turkey) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
Area | Expedition | Conference | ICPR | ||
Notes | DAG; HUPBA; MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ FEL2010 | Serial | 1421 | ||
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Author | Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa | ||||
Title | Multiple-target tracking for the intelligent headlights control | Type | Conference Article | ||
Year | 2010 | Publication | 13th Annual International Conference on Intelligent Transportation Systems | Abbreviated Journal | |
Volume | Issue | Pages | 903–910 | ||
Keywords | Intelligent Headlights | ||||
Abstract | TA7.4
Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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Address | Madeira Island (Portugal) | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ITSC | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ RSL2010 | Serial | 1422 | ||
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Author | Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez | ||||
Title | Vehicle geolocalization based on video synchronization | Type | Conference Article | ||
Year | 2010 | Publication | 13th Annual International Conference on Intelligent Transportation Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1511–1516 | ||
Keywords | video alignment | ||||
Abstract | TC8.6
This paper proposes a novel method for estimating the geospatial localization of a vehicle. I uses as input a georeferenced video sequence recorded by a forward-facing camera attached to the windscreen. The core of the proposed method is an on-line video synchronization which finds out the corresponding frame in the georeferenced video sequence to the one recorded at each time by the camera on a second drive through the same track. Once found the corresponding frame in the georeferenced video sequence, we transfer its geospatial information of this frame. The key advantages of this method are: 1) the increase of the update rate and the geospatial accuracy with regard to a standard low-cost GPS and 2) the ability to localize a vehicle even when a GPS is not available or is not reliable enough, like in certain urban areas. Experimental results for an urban environments are presented, showing an average of relative accuracy of 1.5 meters. |
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Address | Madeira Island (Portugal) | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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Series Volume | Series Issue | Edition | |||
ISSN | 2153-0009 | ISBN | 978-1-4244-7657-2 | Medium | |
Area | Expedition | Conference | ITSC | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ DPS2010 | Serial | 1423 | ||
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Author | Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez | ||||
Title | Vision-based road detection via on-line video registration | Type | Conference Article | ||
Year | 2010 | Publication | 13th Annual International Conference on Intelligent Transportation Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1135–1140 | ||
Keywords | video alignment; road detection | ||||
Abstract | TB6.2
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a traffic-free reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminant-invariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region. |
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Address | Madeira Island (Portugal) | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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Series Volume | Series Issue | Edition | |||
ISSN | 2153-0009 | ISBN | 978-1-4244-7657-2 | Medium | |
Area | Expedition | Conference | ITSC | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ DAS2010 | Serial | 1424 | ||
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Author | Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez | ||||
Title | Camera Egomotion Estimation in the ADAS Context | Type | Conference Article | ||
Year | 2010 | Publication | 13th International IEEE Annual Conference on Intelligent Transportation Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1415–1420 | ||
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Abstract | Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. | ||||
Address | Madeira Island (Portugal) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title ![]() |
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Series Volume | Series Issue | Edition | |||
ISSN | 2153-0009 | ISBN | 978-1-4244-7657-2 | Medium | |
Area | Expedition | Conference | ITSC | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ CPL2010 | Serial | 1425 | ||
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