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Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
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Title |
Learning a Part-based Pedestrian Detector in Virtual World |
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2014 |
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IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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Volume |
15 |
Issue |
5 |
Pages |
2121-2131 |
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Domain Adaptation; Pedestrian Detection; Virtual Worlds |
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Abstract |
Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. |
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1931-0587 |
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978-1-4673-2754-1 |
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ADAS; 600.076 |
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ADAS @ adas @ XVL2014 |
Serial |
2433 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Domain Adaptation of Deformable Part-Based Models |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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36 |
Issue |
12 |
Pages |
2367-2380 |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. |
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0162-8828 |
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ADAS; 600.057; 600.054; 601.217; 600.076 |
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ADAS @ adas @ XRV2014b |
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2436 |
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David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Virtual and Real World Adaptation for Pedestrian Detection |
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Journal Article |
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2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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36 |
Issue |
4 |
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797-809 |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
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0162-8828 |
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ADAS; 600.057; 600.054; 600.076 |
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ADAS @ adas @ VML2014 |
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2275 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Hierarchical Adaptive Structural SVM for Domain Adaptation |
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Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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119 |
Issue |
2 |
Pages |
159-178 |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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Springer US |
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0920-5691 |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XRV2016 |
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2669 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
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Title |
Decremental generalized discriminative common vectors applied to images classification |
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Journal Article |
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2017 |
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Knowledge-Based Systems |
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KBS |
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131 |
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46-57 |
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Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
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In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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ADAS; 600.118; 600.121 |
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Admin @ si @ DMH2017a |
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3003 |
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Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure |
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Title |
3D Perception With Slanted Stixels on GPU |
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Journal Article |
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2021 |
Publication |
IEEE Transactions on Parallel and Distributed Systems |
Abbreviated Journal |
TPDS |
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32 |
Issue |
10 |
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2434-2447 |
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Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure |
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This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. |
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ADAS; 600.124; 600.118 |
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Admin @ si @ HEV2021 |
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3561 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Variance reduction techniques in particle-based visual contour Tracking |
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2009 |
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Pattern Recognition |
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PR |
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42 |
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11 |
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2372–2391 |
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Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
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This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
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ADAS |
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ADAS @ adas @ PoL2009a |
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1168 |
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M. Olivera; Angel Sappa; Victor Santos |
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A probabilistic approach for color correction in image mosaicking applications |
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2015 |
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IEEE Transactions on Image Processing |
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TIP |
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14 |
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2 |
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508 - 523 |
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Color correction; image mosaicking; color transfer; color palette mapping functions |
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Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures. |
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1057-7149 |
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ADAS; 600.076 |
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Admin @ si @ OSS2015b |
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2554 |
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David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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2017 |
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Journal of Healthcare Engineering |
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JHCE |
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2040-2295 |
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Colonoscopy images; Deep Learning; Semantic Segmentation |
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Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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VBS2017b |
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2940 |
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Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
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Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
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2022 |
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Applied Sciences |
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APPLSCI |
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12 |
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2298 |
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Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
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The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
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February 2022 |
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IAM; ADAS; 600.139; 600.145; 600.118 |
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Admin @ si @ HYF2022 |
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3720 |
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