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Author Zhijie Fang; Antonio Lopez edit  url
doi  openurl
  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  
  Keywords (up)  
  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 Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
  Volume 13 Issue 4 Pages 99-103  
  Keywords (up)  
  Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
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  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
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Author Fei Yang; Luis Herranz; Joost van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov edit   pdf
url  doi
openurl 
  Title Variable Rate Deep Image Compression with Modulated Autoencoder Type Journal Article
  Year 2020 Publication IEEE Signal Processing Letters Abbreviated Journal SPL  
  Volume 27 Issue Pages 331-335  
  Keywords (up)  
  Abstract Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.  
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  Notes LAMP; ADAS; 600.141; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ YHW2020 Serial 3346  
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Author Gabriel Villalonga; Joost van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
  Year 2020 Publication Sensors Abbreviated Journal SENS  
  Volume 20 Issue 3 Pages 583  
  Keywords (up)  
  Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.  
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  Notes LAMP; ADAS; 600.118; 600.120 Approved no  
  Call Number Admin @ si @ VWL2020 Serial 3405  
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Author Gabriel Villalonga; Antonio Lopez edit   pdf
doi  openurl
  Title Co-Training for On-Board Deep Object Detection Type Journal Article
  Year 2020 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume Issue Pages 194441 - 194456  
  Keywords (up)  
  Abstract Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ ViL2020 Serial 3488  
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Author Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Multimodal end-to-end autonomous driving Type Journal Article
  Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume Issue Pages 1-11  
  Keywords (up)  
  Abstract A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ XCG2020 Serial 3490  
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Author Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost van de Weijer; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Distributed Learning and Inference with Compressed Images Type Journal Article
  Year 2021 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 30 Issue Pages 3069 - 3083  
  Keywords (up)  
  Abstract Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.  
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  Notes LAMP; ADAS; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ KEH2021 Serial 3543  
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Author Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat edit  url
doi  openurl
  Title Monitoring war destruction from space using machine learning Type Journal Article
  Year 2021 Publication Proceedings of the National Academy of Sciences of the United States of America Abbreviated Journal PNAS  
  Volume 118 Issue 23 Pages e2025400118  
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  Abstract Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ MGH2021 Serial 3584  
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Author Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti edit   pdf
doi  openurl
  Title Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences Type Journal Article
  Year 2011 Publication IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control Abbreviated Journal T-UFFC  
  Volume 58 Issue 1 Pages 60-72  
  Keywords (up) 3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging  
  Abstract Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals.  
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  Series Volume Series Issue Edition  
  ISSN 0885-3010 ISBN Medium  
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  Notes IAM;ADAS Approved no  
  Call Number IAM @ iam @ HGG2011 Serial 1546  
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Author Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras edit  doi
openurl 
  Title Multi-part body segmentation based on depth maps for soft biometry analysis Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 56 Issue Pages 14-21  
  Keywords (up) 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis  
  Abstract This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.  
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  Notes HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB Approved no  
  Call Number Admin @ si @ MEG2015 Serial 2588  
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