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Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers |
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Title |
Adapting Pedestrian Detection from Synthetic to Far Infrared Images |
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Conference Article |
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Year |
2013 |
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ICCV Workshop on Visual Domain Adaptation and Dataset Bias |
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Domain Adaptation; Far Infrared; Pedestrian Detection |
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Abstract |
We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. |
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Sydney; Australia; December 2013 |
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Sydney, Australy |
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English |
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ICCVW-VisDA |
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ADAS; 600.054; 600.055; 600.057; 601.217;ISE |
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Call Number |
ADAS @ adas @ SRV2013 |
Serial |
2334 |
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Author |
Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez |
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Title |
Multi-task Bilinear Classifiers for Visual Domain Adaptation |
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Conference Article |
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Year |
2013 |
Publication |
Advances in Neural Information Processing Systems Workshop |
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Keywords |
Domain Adaptation; Pedestrian Detection; ADAS |
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We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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Lake Tahoe; Nevada; USA; December 2013 |
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NIPSW |
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ADAS; 600.054; 600.057; 601.217;ISE |
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ADAS @ adas @ XRH2013 |
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2340 |
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Author |
David Geronimo; Frederic Lerasle; Antonio Lopez |
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Title |
State-driven particle filter for multi-person tracking |
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Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
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7517 |
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Pages |
467-478 |
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Keywords |
human tracking |
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Abstract |
Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences. |
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Brno, Chzech Republic |
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Springer |
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Heidelberg |
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J. Blanc-Talon et al. |
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English |
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ACIVS |
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ADAS |
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yes |
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Call Number |
GLL2012; ADAS @ adas @ gll2012a |
Serial |
1990 |
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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 |
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Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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3492 - 3495 |
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Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
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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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Tsukuba Science City, Japan |
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IEEE |
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Tsukuba Science City, JAPAN |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Monocular Egomotion Estimation based on Image Matching |
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Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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425-430 |
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SLAM |
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Portugal |
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ICPRAM |
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ADAS |
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no |
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Admin @ si @ CPL2012a;; ADAS @ adas @ |
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2011 |
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Author |
Fernando Barrera; Felipe Lumbreras; Cristhian Aguilera; Angel Sappa |
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Title |
Planar-Based Multispectral Stereo |
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Conference Article |
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2012 |
Publication |
11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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ADAS |
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no |
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Admin @ si @ BLA2012 |
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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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2012 |
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11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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ADAS; TV |
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Admin @ si @ ABS2012 |
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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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2012 |
Publication |
Adaptive Learning Agents Workshop |
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33-39 |
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Valencia |
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ALA |
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ADAS; RV |
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Admin @ si @ PSL2012 |
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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 |
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2012 |
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IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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SLAM |
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Alcalá de Henares |
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IVW |
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ADAS |
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Admin @ si @ RSP2012; ADAS @ adas |
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2019 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Multiple target tracking and identity linking under split, merge and occlusion of targets and observations |
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2012 |
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1st International Conference on Pattern Recognition Applications and Methods |
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Algarve, Portugal |
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ICPRAM |
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ADAS |
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Admin @ si @ RSL2012c; ADAS @ adas |
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2034 |
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