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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 |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Pages |
2592 - 2599 |
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Keywords |
ADAS; Random Forest; Pedestrian Detection |
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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. |
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Sydney; Australia; December 2013 |
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IEEE |
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ISSN |
1550-5499 |
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Conference |
ICCV |
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Notes |
ADAS; 600.057; 600.054 |
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no |
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Call Number |
ADAS @ adas @ MVL2013 |
Serial |
2333 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
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Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
3492 - 3495 |
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Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
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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). |
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Address |
Tsukuba Science City, Japan |
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IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
ADAS |
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no |
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Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
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Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
2664 - 2667 |
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Abstract |
We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
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Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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ICPR |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012a; |
Serial |
2032 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Monocular Egomotion Estimation based on Image Matching |
Type |
Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
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Volume |
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Pages |
425-430 |
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Keywords |
SLAM |
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Address |
Portugal |
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ICPRAM |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ CPL2012a;; ADAS @ adas @ |
Serial |
2011 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Monocular Depth-based Background Estimation |
Type |
Conference Article |
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Year |
2012 |
Publication |
7th International Conference on Computer Vision Theory and Applications |
Abbreviated Journal |
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Issue |
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Pages |
323-328 |
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Abstract |
In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences. |
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Address |
Roma |
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Conference |
VISAPP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e |
Serial |
2012 |
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Permanent link to this record |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Pedestrian Candidates Generation using Monocular Cues |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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Pages |
7-12 |
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Keywords |
pedestrian detection |
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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. |
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Publisher |
IEEE Xplore |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
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Conference |
IV |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
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Author |
Fernando Barrera; Felipe Lumbreras; Cristhian Aguilera; Angel Sappa |
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Title |
Planar-Based Multispectral Stereo |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th Quantitative InfraRed Thermography |
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Address |
Naples, Italy |
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QIRT |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ BLA2012 |
Serial |
2016 |
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Author |
Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo |
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Title |
A Novel SIFT-Like-Based Approach for FIR-VS Images Registration |
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Conference Article |
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Year |
2012 |
Publication |
11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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Notes |
ADAS; TV |
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no |
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Call Number |
Admin @ si @ ABS2012 |
Serial |
2017 |
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Author |
Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo |
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Title |
Feature Selection Based on Reinforcement Learning for Object Recognition |
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Conference Article |
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Year |
2012 |
Publication |
Adaptive Learning Agents Workshop |
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33-39 |
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Address |
Valencia |
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ALA |
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Notes |
ADAS; RV |
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no |
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Call Number |
Admin @ si @ PSL2012 |
Serial |
2018 |
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Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Visual SLAM for Driverless Cars: A Brief Survey |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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SLAM |
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Address |
Alcalá de Henares |
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IVW |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ RSP2012; ADAS @ adas |
Serial |
2019 |
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