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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title 3D Scene Priors for Road Detection Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 57–64  
  Keywords road detection  
  Abstract Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.  
  Address San Francisco; CA; USA; June 2010  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN (up) 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ AGL2010a Serial 1302  
Permanent link to this record
 

 
Author Mohammad Rouhani; Angel Sappa edit  doi
isbn  openurl
  Title Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3066-3072  
  Keywords  
  Abstract This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided.  
  Address San Francisco; CA; USA; June 2010  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN (up) 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RoS2010a Serial 1303  
Permanent link to this record
 

 
Author Javier Marin; David Vazquez; David Geronimo; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Learning Appearance in Virtual Scenarios for Pedestrian Detection Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 137–144  
  Keywords Pedestrian Detection; Domain Adaptation  
  Abstract Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance.  
  Address San Francisco; CA; USA; June 2010  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language English Original Title Learning Appearance in Virtual Scenarios for Pedestrian Detection  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN (up) 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVG2010 Serial 1304  
Permanent link to this record
 

 
Author David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo edit  doi
isbn  openurl
  Title Fast and Robust Object Segmentation with the Integral Linear Classifier Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1046–1053  
  Keywords  
  Abstract We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.  
  Address San Francisco; CA; USA; June 2010  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN (up) 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ ARL2010a Serial 1311  
Permanent link to this record
 

 
Author Jaume Amores edit  doi
isbn  openurl
  Title Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study Type Conference Article
  Year 2010 Publication 20th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 4246–4250  
  Keywords  
  Abstract Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance.  
  Address Istanbul, Turkey  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN (up) 978-1-4244-7542-1 Medium  
  Area Expedition Conference ICPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ Amo2010 Serial 1295  
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Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  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.
 
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN (up) 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DPS2010 Serial 1423  
Permanent link to this record
 

 
Author Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  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.
 
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN (up) 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DAS2010 Serial 1424  
Permanent link to this record
 

 
Author Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez edit   pdf
doi  isbn
openurl 
  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  
  Keywords  
  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 Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN (up) 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ CPL2010 Serial 1425  
Permanent link to this record
 

 
Author Fernando Barrera; Felipe Lumbreras; Angel Sappa edit  doi
isbn  openurl
  Title Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space Type Conference Article
  Year 2010 Publication 17th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 2749–2752  
  Keywords  
  Abstract This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach.  
  Address Hong-Kong  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1522-4880 ISBN (up) 978-1-4244-7992-4 Medium  
  Area Expedition Conference ICIP  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ BLS2010 Serial 1358  
Permanent link to this record
 

 
Author Mohammad Rouhani; Angel Sappa edit  doi
isbn  openurl
  Title A Fast accurate Implicit Polynomial Fitting Approach Type Conference Article
  Year 2010 Publication 17th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 1429–1432  
  Keywords  
  Abstract This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons.  
  Address Hong-Kong  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1522-4880 ISBN (up) 978-1-4244-7992-4 Medium  
  Area Expedition Conference ICIP  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RoS2010b Serial 1359  
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