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Jose Luis Gomez, Manuel Silva, Antonio Seoane, Agnes Borras, Mario Noriega, German Ros, et al. (2023). All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes.
Abstract: We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (this http URL).
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Yainuvis Socarras. (2011). Image segmentation for improving pedestrian detection (Vol. 167). Master's thesis, , .
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German Ros, J. Guerrero, Angel Sappa, & Antonio Lopez. (2013). VSLAM pose initialization via Lie groups and Lie algebras optimization. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 5740–5747).
Abstract: We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.
Keywords: SLAM
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Yainuvis Socarras, Sebastian Ramos, David Vazquez, Antonio Lopez, & Theo Gevers. (2013). Adapting Pedestrian Detection from Synthetic to Far Infrared Images. In ICCV Workshop on Visual Domain Adaptation and Dataset Bias. Sydney, Australy.
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.
Keywords: Domain Adaptation; Far Infrared; Pedestrian Detection
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Fernando Barrera, Felipe Lumbreras, & Angel Sappa. (2013). Multispectral Piecewise Planar Stereo using Manhattan-World Assumption. PRL - Pattern Recognition Letters, 34(1), 52–61.
Abstract: This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework.
Keywords: Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images
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Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin, & Daniel Ponsa. (2013). Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection. In IEEE Intelligent Vehicles Symposium (pp. 467–472). IEEE.
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).
Keywords: Pedestrian Detection; Virtual World; Part based
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David Vazquez, Jiaolong Xu, Sebastian Ramos, Antonio Lopez, & Daniel Ponsa. (2013). Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes. In CVPR Workshop on Ground Truth – What is a good dataset? (pp. 706–711). IEEE.
Abstract: Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
Keywords: Pedestrian Detection; Domain Adaptation
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Jiaolong Xu, David Vazquez, Sebastian Ramos, Antonio Lopez, & Daniel Ponsa. (2013). Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers. In CVPR Workshop on Ground Truth – What is a good dataset? (pp. 688–693).
Abstract: Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.
Keywords: Pedestrian Detection; Domain Adaptation
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Jiaolong Xu, Sebastian Ramos, Xu Hu, David Vazquez, & Antonio Lopez. (2013). Multi-task Bilinear Classifiers for Visual Domain Adaptation. In Advances in Neural Information Processing Systems Workshop.
Abstract: 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.
Keywords: Domain Adaptation; Pedestrian Detection; ADAS
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Andrew Nolan, Daniel Serrano, Aura Hernandez-Sabate, Daniel Ponsa, & Antonio Lopez. (2013). Obstacle mapping module for quadrotors on outdoor Search and Rescue operations. In International Micro Air Vehicle Conference and Flight Competition.
Abstract: Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.
Keywords: UAV
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Marcelo D. Pistarelli, Angel Sappa, & Ricardo Toledo. (2013). Multispectral Stereo Image Correspondence. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 217–224). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
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Gioacchino Vino, & Angel Sappa. (2013). Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. In 10th International Conference on Image Analysis and Recognition (Vol. 7950, pp. 354–363). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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Joan Marc Llargues Asensio, Juan Peralta, Raul Arrabales, Manuel Gonzalez Bedia, Paulo Cortez, & Antonio Lopez. (2014). Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters. EXSY - Expert Systems With Applications, 41(16), 7281–7290.
Abstract: Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.
Keywords: Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks
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Monica Piñol, Angel Sappa, & Ricardo Toledo. (2015). Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy. NEUCOM - Neurocomputing, 150(A), 106–115.
Abstract: This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
Keywords: Reinforcement learning; Q-learning; Bag of features; Descriptors
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P. Ricaurte, C. Chilan, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, & Angel Sappa. (2014). Performance Evaluation of Feature Point Descriptors in the Infrared Domain. In 9th International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 545–550).
Abstract: This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
Keywords: Infrared Imaging; Feature Point Descriptors
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