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Author David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo edit  url
isbn  openurl
  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.  
  Address  
  Corporate Author Thesis  
  Publisher (down) IOS Press Amsterdam, Place of Publication Editor In R.Alquezar, A.Moreno, J.Aguilar.  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 9781607506423 Medium  
  Area Expedition Conference CCIA  
  Notes ADAS Approved no  
  Call Number Admin @ si @ ARL2010b Serial 1417  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez edit   pdf
url  doi
isbn  openurl
  Title Color Attributes for Object Detection Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3306-3313  
  Keywords pedestrian detection  
  Abstract State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.		  
  Address Providence; Rhode Island; USA;  
  Corporate Author Thesis  
  Publisher (down) IEEE Xplore 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 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS; CIC; Approved no  
  Call Number Admin @ si @ KRW2012 Serial 1935  
Permanent link to this record
 
 
Author Diego Cheda; Daniel Ponsa; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Pedestrian Candidates Generation using Monocular Cues Type Conference Article
  Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 7-12  
  Keywords pedestrian detection  
  Abstract Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.  
  Address  
  Corporate Author Thesis  
  Publisher (down) IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium  
  Area Expedition Conference IV  
  Notes ADAS Approved no  
  Call Number Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d Serial 2013  
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Author Naveen Onkarappa; Angel Sappa edit   pdf
doi  isbn
openurl 
  Title An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed Type Conference Article
  Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 1138-1143  
  Keywords  
  Abstract Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow.  
  Address Alcalá de Henares  
  Corporate Author Thesis  
  Publisher (down) IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium  
  Area Expedition Conference IV  
  Notes ADAS Approved no  
  Call Number Admin @ si @ NaS2012 Serial 2020  
Permanent link to this record
 
 
Author Miguel Oliveira; Angel Sappa; V. Santos edit   pdf
doi  isbn
openurl 
  Title Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models Type Conference Article
  Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 299-303  
  Keywords  
  Abstract The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.  
  Address Alcalá de Henares  
  Corporate Author Thesis  
  Publisher (down) IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium  
  Area Expedition Conference IV  
  Notes ADAS Approved no  
  Call Number Admin @ si @ OSS2012b Serial 2021  
Permanent link to this record
 
 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Unsupervised co-segmentation through region matching Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 749-756  
  Keywords  
  Abstract Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.  
  Address Providence, Rhode Island  
  Corporate Author Thesis  
  Publisher (down) IEEE Xplore 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 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSL2012b; ADAS @ adas @ Serial 2033  
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Author Patricia Marquez; Debora Gil; Aura Hernandez-Sabate edit   pdf
url  doi
openurl 
  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.  
  Address  
  Corporate Author Thesis  
  Publisher (down) IEEE Place of Publication Barcelona (Spain) Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCVW  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ MGH2011 Serial 1682  
Permanent link to this record
 
 
Author Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe edit   pdf
doi  openurl
  Title Random Forests of Local Experts for Pedestrian Detection Type Conference Article
  Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 2592 - 2599  
  Keywords ADAS; Random Forest; Pedestrian Detection  
  Abstract Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.  
  Address Sydney; Australia; December 2013  
  Corporate Author Thesis  
  Publisher (down) IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1550-5499 ISBN Medium  
  Area Expedition Conference ICCV  
  Notes ADAS; 600.057; 600.054 Approved no  
  Call Number ADAS @ adas @ MVL2013 Serial 2333  
Permanent link to this record
 
 
Author David Vazquez; Antonio Lopez; Daniel Ponsa edit   pdf
isbn  openurl
  Title Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3492 - 3495  
  Keywords Pedestrian Detection; Domain Adaptation; Virtual worlds  
  Abstract Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1).  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher (down) IEEE Place of Publication Tsukuba Science City, JAPAN Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ VLP2012 Serial 1981  
Permanent link to this record
 
 
Author Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa edit   pdf
doi  isbn
openurl 
  Title Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection Type Conference Article
  Year 2013 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 467 - 472  
  Keywords Pedestrian Detection; Virtual World; Part based  
  Abstract State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster).  
  Address Gold Coast; Australia; June 2013  
  Corporate Author Thesis  
  Publisher (down) IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1931-0587 ISBN 978-1-4673-2754-1 Medium  
  Area Expedition Conference IV  
  Notes ADAS; 600.054; 600.057 Approved no  
  Call Number XVL2013; ADAS @ adas @ xvl2013a Serial 2214  
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