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Author | David Geronimo; Antonio Lopez | ||||
Title | Vision-based Pedestrian Protection Systems for Intelligent Vehicles | Type | Book Whole | ||
Year | 2014 | Publication | SpringerBriefs in Computer Science | Abbreviated Journal | |
Volume | Issue | Pages | 1-114 | ||
Keywords | Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users | ||||
Abstract | Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. | ||||
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Corporate Author | Thesis | ||||
Publisher | Springer Briefs in Computer Vision | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-4614-7986-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | GeL2014 | Serial | 2325 | ||
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Author | Monica Piñol | ||||
Title | Reinforcement Learning of Visual Descriptors for Object Recognition | Type | Book Whole | ||
Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ricardo Toledo;Angel Sappa | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-940902-5-7 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Piñ2014 | Serial | 2464 | ||
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Author | Jiaolong Xu | ||||
Title | Domain Adaptation of Deformable Part-based Models | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection. The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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Address | April 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Antonio Lopez | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-943427-1-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Xu2015 | Serial | 2631 | ||
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Author | Alejandro Gonzalez Alzate | ||||
Title | Multi-modal Pedestrian Detection | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier. We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems. Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance. |
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Address | November 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | David Vazquez;Antonio Lopez; | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-943427-7-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Gon2015 | Serial | 2706 | ||
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Author | Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez | ||||
Title | Computer Vision in Vehicle Technology: Land, Sea & Air | Type | Book Whole | ||
Year | 2017 | Publication | Abbreviated Journal | ||
Volume | Issue | Pages | 161-163 | ||
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Abstract | Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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Corporate Author | Thesis | ||||
Publisher | John Wiley & Sons, Ltd | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-118-86807-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ LIP2017a | Serial | 2937 | ||
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Author | Cristhian Aguilera | ||||
Title | Local feature description in cross-spectral imagery | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing.
The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band. The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies. |
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Address | October 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-945373-6-3 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Agu2017 | Serial | 3020 | ||
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Author | Cesar de Souza | ||||
Title | Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video | Type | Book Whole | ||
Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
Address | April 2018 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Naila Murray | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Sou2018 | Serial | 3127 | ||
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Author | Felipe Codevilla | ||||
Title | On Building End-to-End Driving Models Through Imitation Learning | Type | Book Whole | ||
Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Autonomous vehicles are now considered as an assured asset in the future. Literally, all the relevant car-markers are now in a race to produce fully autonomous vehicles. These car-makers usually make use of modular pipelines for designing autonomous vehicles. This strategy decomposes the problem in a variety of tasks such as object detection and recognition, semantic and instance segmentation, depth estimation, SLAM and place recognition, as well as planning and control. Each module requires a separate set of expert algorithms, which are costly specially in the amount of human labor and necessity of data labelling. An alternative, that recently has driven considerable interest, is the end-to-end driving. In the end-to-end driving paradigm, perception and control are learned simultaneously using a deep network. These sensorimotor models are typically obtained by imitation learning fromhuman demonstrations. The main advantage is that this approach can directly learn from large fleets of human-driven vehicles without requiring a fixed ontology and extensive amounts of labeling. However, scaling end-to-end driving methods to behaviors more complex than simple lane keeping or lead vehicle following remains an open problem. On this thesis, in order to achieve more complex behaviours, we
address some issues when creating end-to-end driving system through imitation learning. The first of themis a necessity of an environment for algorithm evaluation and collection of driving demonstrations. On this matter, we participated on the creation of the CARLA simulator, an open source platformbuilt from ground up for autonomous driving validation and prototyping. Since the end-to-end approach is purely reactive, there is also the necessity to provide an interface with a global planning system. With this, we propose the conditional imitation learning that conditions the actions produced into some high level command. Evaluation is also a concern and is commonly performed by comparing the end-to-end network output to some pre-collected driving dataset. We show that this is surprisingly weakly correlated to the actual driving and propose strategies on how to better acquire data and a better comparison strategy. Finally, we confirmwell-known generalization issues (due to dataset bias and overfitting), new ones (due to dynamic objects and the lack of a causal model), and training instability; problems requiring further research before end-to-end driving through imitation can scale to real-world driving. |
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Address | May 2019 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Cod2019 | Serial | 3387 | ||
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Author | Zhijie Fang | ||||
Title | Behavior understanding of vulnerable road users by 2D pose estimation | Type | Book Whole | ||
Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians
and cyclists can be critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, therefore, should be taken into account by systems providing any level of driving assistance, i.e. from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this PhD work, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow the established traffic codes to indicate future left/right turns and stop maneuvers with arm signals. In the case of pedestrians, no indications can be assumed a priori. Instead, we hypothesize that the walking pattern of a pedestrian can allow us to determine if he/she has the intention of crossing the road in the path of the egovehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this PhD work, we show how the same methodology can be used for recognizing pedestrians and cyclists’ intentions. For pedestrians, we perform experiments on the publicly available Daimler and JAAD datasets. For cyclists, we did not found an analogous dataset, therefore, we created our own one by acquiring and annotating corresponding video-sequences which we aim to share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. |
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Address | May 2019 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;David Vazquez | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-948531-6-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Fan2019 | Serial | 3388 | ||
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Author | Gabriel Villalonga | ||||
Title | Leveraging Synthetic Data to Create Autonomous Driving Perception Systems | Type | Book Whole | ||
Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Manually annotating images to develop vision models has been a major bottleneck
since computer vision and machine learning started to walk together. This has been more evident since computer vision falls on the shoulders of data-hungry deep learning techniques. When addressing on-board perception for autonomous driving, the curse of data annotation is exacerbated due to the use of additional sensors such as LiDAR. Therefore, any approach aiming at reducing such a timeconsuming and costly work is of high interest for addressing autonomous driving and, in fact, for any application requiring some sort of artificial perception. In the last decade, it has been shown that leveraging from synthetic data is a paradigm worth to pursue in order to minimizing manual data annotation. The reason is that the automatic process of generating synthetic data can also produce different types of associated annotations (e.g. object bounding boxes for synthetic images and LiDAR pointclouds, pixel/point-wise semantic information, etc.). Directly using synthetic data for training deep perception models may not be the definitive solution in all circumstances since it can appear a synth-to-real domain shift. In this context, this work focuses on leveraging synthetic data to alleviate manual annotation for three perception tasks related to driving assistance and autonomous driving. In all cases, we assume the use of deep convolutional neural networks (CNNs) to develop our perception models. The first task addresses traffic sign recognition (TSR), a kind of multi-class classification problem. We assume that the number of sign classes to be recognized must be suddenly increased without having annotated samples to perform the corresponding TSR CNN re-training. We show that leveraging synthetic samples of such new classes and transforming them by a generative adversarial network (GAN) trained on the known classes (i.e. without using samples from the new classes), it is possible to re-train the TSR CNN to properly classify all the signs for a ∼ 1/4 ratio of new/known sign classes. The second task addresses on-board 2D object detection, focusing on vehicles and pedestrians. In this case, we assume that we receive a set of images without the annotations required to train an object detector, i.e. without object bounding boxes. Therefore, our goal is to self-annotate these images so that they can later be used to train the desired object detector. In order to reach this goal, we leverage from synthetic data and propose a semi-supervised learning approach based on the co-training idea. In fact, we use a GAN to reduce the synthto-real domain shift before applying co-training. Our quantitative results show that co-training and GAN-based image-to-image translation complement each other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from human annotations. While in previous tasks we focus on vision-based perception, the third task we address focuses on LiDAR pointclouds. Our initial goal was to develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While for images we may expect synth/real-to-real domain shift due to differences in their appearance (e.g. when source and target images come from different camera sensors), we did not expect so for LiDAR pointclouds since these active sensors factor out appearance and provide sampled shapes. However, in practice, we have seen that it can be domain shift even among real-world LiDAR pointclouds. Factors such as the sampling parameters of the LiDARs, the sensor suite configuration onboard the ego-vehicle, and the human annotation of 3D bounding boxes, do induce a domain shift. We show it through comprehensive experiments with different publicly available datasets and 3D detectors. This redirected our goal towards the design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored topic. Finally, it is worth to mention that all the synthetic datasets used for these three tasks, have been designed and generated in the context of this PhD work and will be publicly released. Overall, we think this PhD presents several steps forward to encourage leveraging synthetic data for developing deep perception models in the field of driving assistance and autonomous driving. |
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Address | February 2021 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;German Ros | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-122714-2-3 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ Vil2021 | Serial | 3599 | ||
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Author | Angel Sappa; Jordi Vitria | ||||
Title | Multimodal Interaction in Image and Video Applications | Type | Book Whole | ||
Year | 2013 | Publication | Multimodal Interaction in Image and Video Applications | Abbreviated Journal | |
Volume | 48 | Issue | Pages | ||
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Abstract | Book Series Intelligent Systems Reference Library | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
Area | Expedition | Conference | |||
Notes | ADAS; OR;MV | Approved | no | ||
Call Number | Admin @ si @ SaV2013 | Serial | 2199 | ||
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Author | Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek | ||||
Title | Color in Computer Vision: Fundamentals and Applications | Type | Book Whole | ||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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Publisher | The Wiley-IS&T Series in Imaging Science and Technology | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ GGG2012a | Serial | 2068 | ||
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Author | Ramon Baldrich | ||||
Title | Perceptual approach to a computational colour-texture representation for surface inspection. | Type | Book Whole | ||
Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ Bal2001 | Serial | 73 | ||
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Author | Robert Benavente | ||||
Title | A Parametric Model for Computational Colour Naming | Type | Book Whole | ||
Year | 2007 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Keywords | PhD Thesis | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
Language | Summary Language | Original Title | |||
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ Ben2007 | Serial | 1108 | ||
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Author | Javier Vazquez | ||||
Title | Colour Constancy in Natural Through Colour Naming and Sensor Sharpening | Type | Book Whole | ||
Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Colour is derived from three physical properties: incident light, object reflectance and sensor sensitivities. Incident light varies under natural conditions; hence, recovering scene illuminant is an important issue in computational colour. One way to deal with this problem under calibrated conditions is by following three steps, 1) building a narrow-band sensor basis to accomplish the diagonal model, 2) building a feasible set of illuminants, and 3) defining criteria to select the best illuminant. In this work we focus on colour constancy for natural images by introducing perceptual criteria in the first and third stages.
To deal with the illuminant selection step, we hypothesise that basic colour categories can be used as anchor categories to recover the best illuminant. These colour names are related to the way that the human visual system has evolved to encode relevant natural colour statistics. Therefore the recovered image provides the best representation of the scene labelled with the basic colour terms. We demonstrate with several experiments how this selection criterion achieves current state-of-art results in computational colour constancy. In addition to this result, we psychophysically prove that usual angular error used in colour constancy does not correlate with human preferences, and we propose a new perceptual colour constancy evaluation. The implementation of this selection criterion strongly relies on the use of a diagonal model for illuminant change. Consequently, the second contribution focuses on building an appropriate narrow-band sensor basis to represent natural images. We propose to use the spectral sharpening technique to compute a unique narrow-band basis optimised to represent a large set of natural reflectances under natural illuminants and given in the basis of human cones. The proposed sensors allow predicting unique hues and the World colour Survey data independently of the illuminant by using a compact singularity function. Additionally, we studied different families of sharp sensors to minimise different perceptual measures. This study brought us to extend the spherical sampling procedure from 3D to 6D. Several research lines still remain open. One natural extension would be to measure the effects of using the computed sharp sensors on the category hypothesis, while another might be to insert spatial contextual information to improve category hypothesis. Finally, much work still needs to be done to explore how individual sensors can be adjusted to the colours in a scene. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell;Graham D. Finlayson | |
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Vaz2011a | Serial | 1785 | ||
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