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Author | Maria Salamo; Sergio Escalera | ||||
Title | Increasing Retrieval Quality in Conversational Recommenders | Type | Journal Article | ||
Year | 2011 | Publication ![]() |
IEEE Transactions on Knowledge and Data Engineering | Abbreviated Journal | TKDE |
Volume | 99 | Issue | Pages | 1-1 | |
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Abstract | IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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Publisher | IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 1041-4347 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | MILAB; HuPBA | Approved | no | ||
Call Number | Admin @ si @ SaE2011 | Serial | 1713 | ||
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Author | Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez | ||||
Title | An Efficient Approach to Onboard Stereo Vision System Pose Estimation | Type | Journal Article | ||
Year | 2008 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 9 | Issue | 3 | Pages | 476–490 |
Keywords | Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system | ||||
Abstract | This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. | ||||
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Publisher | IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ SDP2008 | Serial | 1000 | ||
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Author | Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva | ||||
Title | Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification | Type | Journal Article | ||
Year | 2009 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 10 | Issue | 1 | Pages | 113–126 |
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Abstract | The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR;MILAB;HuPBA;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BEV2008 | Serial | 1116 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Road Detection Based on Illuminant Invariance | Type | Journal Article | ||
Year | 2011 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 12 | Issue | 1 | Pages | 184-193 |
Keywords | road detection | ||||
Abstract | By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. | ||||
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Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ AlL2011 | Serial | 1456 | ||
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Author | Fadi Dornaika; Jose Manuel Alvarez; Angel Sappa; Antonio Lopez | ||||
Title | A New Framework for Stereo Sensor Pose through Road Segmentation and Registration | Type | Journal Article | ||
Year | 2011 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 12 | Issue | 4 | Pages | 954-966 |
Keywords | road detection | ||||
Abstract | This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework. | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ DAS2011; ADAS @ adas @ das2011a | Serial | 1833 | ||
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Author | Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa | ||||
Title | Multiple target tracking for intelligent headlights control | Type | Journal Article | ||
Year | 2012 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 13 | Issue | 2 | Pages | 594-605 |
Keywords | Intelligent Headlights | ||||
Abstract | Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g | Serial | 1877 | ||
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Author | Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca | ||||
Title | Toward Real-Time Pedestrian Detection Based on a Deformable Template Model | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 1 | Pages | 355-364 |
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Abstract | Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE; 601.213; 600.078 | Approved | no | ||
Call Number | PGH2014 | Serial | 2350 | ||
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Author | Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez | ||||
Title | Road Geometry Classification by Adaptative Shape Models | Type | Journal Article | ||
Year | 2013 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 14 | Issue | 1 | Pages | 459-468 |
Keywords | road detection | ||||
Abstract | Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGD2013;; ADAS @ adas @ | Serial | 2269 | ||
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Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 1 | Pages | 136-147 |
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Abstract | IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields. |
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OnS2014a | Serial | 2386 | ||
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Author | Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa | ||||
Title | Learning a Part-based Pedestrian Detector in Virtual World | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 5 | Pages | 2121-2131 |
Keywords | Domain Adaptation; Pedestrian Detection; Virtual Worlds | ||||
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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Series Volume | Series Issue | Edition | |||
ISSN | 1931-0587 | ISBN | 978-1-4673-2754-1 | Medium | |
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ XVL2014 | Serial | 2433 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras | ||||
Title | Combining Priors, Appearance and Context for Road Detection | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 3 | Pages | 1168-1178 |
Keywords | Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout | ||||
Abstract | Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
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Publisher | Place of Publication | Editor | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076;ISE | Approved | no | ||
Call Number | Admin @ si @ ALG2014 | Serial | 2501 | ||
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Author | T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo | ||||
Title | Multi-Spectral Stereo Odometry | Type | Journal Article | ||
Year | 2015 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 16 | Issue | 3 | Pages | 1210-1224 |
Keywords | Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery | ||||
Abstract | In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated. |
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ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ MAS2015a | Serial | 2533 | ||
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Author | Zhijie Fang; Antonio Lopez | ||||
Title | Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation | Type | Journal Article | ||
Year | 2019 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 21 | Issue | 11 | Pages | 4773 - 4783 |
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Abstract | Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, 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 traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we 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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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ FaL2019 | Serial | 3305 | ||
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Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier | ||||
Title | Effects of Non-Driving Related Tasks during Self-Driving mode | Type | Journal Article | ||
Year | 2022 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 23 | Issue | 2 | Pages | 1391-1399 |
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Abstract | Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study. | ||||
Address | Feb. 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ MHE2022 | Serial | 3468 | ||
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Author | Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez | ||||
Title | Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision | Type | Journal Article | ||
Year | 2021 | Publication ![]() |
IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 23 | Issue | 8 | Pages | 12738-12751 |
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Abstract | Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GTS2021 | Serial | 3598 | ||
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