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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title (down) Learning Photometric Invariance from Diversified Color Model Ensembles Type Conference Article
  Year 2009 Publication 22nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 565–572  
  Keywords road detection  
  Abstract Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.  
  Address Miami (USA)  
  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 978-1-4244-3992-8 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ AGL2009 Serial 1169  
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Author Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder edit   pdf
url  doi
openurl 
  Title (down) Learning Multi-Object Tracking and Segmentation from Automatic Annotations Type Conference Article
  Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 6845-6854  
  Keywords  
  Abstract In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data.  
  Address virtual; June 2020  
  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 ISBN Medium  
  Area Expedition Conference CVPR  
  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ PHR2020 Serial 3402  
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Author Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk edit   pdf
openurl 
  Title (down) Learning local feature descriptors with triplets and shallow convolutional neural networks Type Conference Article
  Year 2016 Publication 27th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.		  
  Address York; UK; September 2016  
  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 ISBN Medium  
  Area Expedition Conference BMVC  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ BRP2016 Serial 2818  
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Author Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo edit   pdf
doi  openurl
  Title (down) Learning cross-spectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.  
  Address Las vegas; USA; June 2016  
  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 ISBN Medium  
  Area Expedition Conference CVPRW  
  Notes ADAS; 600.086; 600.076 Approved no  
  Call Number Admin @ si @AAS2016 Serial 2809  
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Author Javier Marin; David Vazquez; David Geronimo; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title (down) 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 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVG2010 Serial 1304  
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Author Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa edit   pdf
doi  isbn
openurl 
  Title (down) 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 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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Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title (down) Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario Type Conference Article
  Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 8048 Issue Pages 483-490  
  Keywords Optical flow; regularization; Driver Assistance Systems; Performance Evaluation  
  Abstract Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).  
  Address York; UK; August 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium  
  Area Expedition Conference CAIP  
  Notes ADAS; 600.055; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013b Serial 2244  
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Author Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu edit   pdf
doi  openurl
  Title (down) Joint coarse-and-fine reasoning for deep optical flow Type Conference Article
  Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 2558-2562  
  Keywords  
  Abstract We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.  
  Address Beijing; China; September 2017  
  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 ISBN Medium  
  Area Expedition Conference ICIP  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ VRM2017 Serial 2898  
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Author Zhijie Fang; Antonio Lopez edit   pdf
url  doi
openurl 
  Title (down) Is the Pedestrian going to Cross? Answering by 2D Pose Estimation Type Conference Article
  Year 2018 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 1271 - 1276  
  Keywords  
  Abstract Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results.  
  Address  
  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 ISBN Medium  
  Area Expedition Conference IV  
  Notes ADAS; 600.124; 600.116; 600.118 Approved no  
  Call Number Admin @ si @ FaL2018 Serial 3181  
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Author Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez edit   pdf
openurl 
  Title (down) Invertible conditional gans for image editing Type Conference Article
  Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.		  
  Address Barcelona; Spain; December 2016  
  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 ISBN Medium  
  Area Expedition Conference NIPSW  
  Notes LAMP; ADAS; 600.068 Approved no  
  Call Number Admin @ si @ PWR2016 Serial 2906  
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