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Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
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Conference Article |
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Year |
2013 |
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15th IEEE International Conference on Computer Vision |
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2312 - 2319 |
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Semantic Segmentation |
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Abstract |
Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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ADAS; 600.057 |
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ADAS @ adas @ RBN2013 |
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2377 |
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Author |
Petia Radeva; Joan Serrat |
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Title |
Rubber Snake: Implementation on Signed Distance Potential. |
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1993 |
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Vision Conference |
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187-194 |
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Zurich, Switzerland. |
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ADAS;MILAB |
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ADAS @ adas @ RaS1993 |
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170 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Cascade of Classifiers for Vehicle Detection |
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Conference Article |
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Year |
2007 |
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Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989 |
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vehicle detection |
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Delft (Netherlands) |
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ADAS |
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ADAS @ adas @ PoL2007c |
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935 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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47-54 |
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Girona (Spain) |
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ADAS |
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ADAS @ adas @ PoL2007b |
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787 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Vehicle Trajectory Estimation based on Monocular Vision |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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587-594 |
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vehicle detection |
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Girona (Spain) |
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ADAS |
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ADAS @ adas @ PoL2007a |
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785 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
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Conference Article |
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Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
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6111 |
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230-239 |
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This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Povoa de Varzim (Portugal) |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-13771-6 |
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ICIAR |
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ADAS |
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ADAS @ adas @ OnS2010 |
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1342 |
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Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
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Title |
On-line Semantic Perception Using Uncertainty |
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Conference Article |
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Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Keywords |
Semantic Segmentation |
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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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IROS |
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ADAS |
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ADAS @ adas @ NRR2012 |
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2378 |
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Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe |
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Title |
Random Forests of Local Experts for Pedestrian Detection |
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Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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2592 - 2599 |
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Keywords |
ADAS; Random Forest; Pedestrian Detection |
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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. |
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Sydney; Australia; December 2013 |
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IEEE |
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1550-5499 |
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ICCV |
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ADAS; 600.057; 600.054 |
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ADAS @ adas @ MVL2013 |
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2333 |
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Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
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Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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Conference Article |
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Year |
2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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137–144 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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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. |
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San Francisco; CA; USA; June 2010 |
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English |
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English |
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Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS |
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ADAS @ adas @ MVG2010 |
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1304 |
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Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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Keywords |
lane markings |
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Girona (Spain) |
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J. Marti et al |
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IbPRIA |
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ADAS |
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ADAS @ adas @ LSC2007 |
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881 |
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