| Records |
| Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
| Title |
Pedestrian Candidates Generation using Monocular Cues |
Type |
Conference Article |
| Year |
2012 |
Publication  |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
7-12 |
| Keywords |
pedestrian detection |
| Abstract |
Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. |
| Address |
|
| Corporate Author |
|
Thesis |
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| Publisher |
IEEE Xplore |
Place of Publication |
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Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
| Permanent link to this record |
| |
|
| |
| Author |
Naveen Onkarappa; Angel Sappa |
| Title |
An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed |
Type |
Conference Article |
| Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1138-1143 |
| Keywords |
|
| Abstract |
Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow. |
| Address |
Alcalá de Henares |
| Corporate Author |
|
Thesis |
|
| Publisher |
IEEE Xplore |
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ NaS2012 |
Serial |
2020 |
| Permanent link to this record |
| |
|
| |
| Author |
Miguel Oliveira; Angel Sappa; V. Santos |
| Title |
Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models |
Type |
Conference Article |
| Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
299-303 |
| Keywords |
|
| Abstract |
The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
| Address |
Alcalá de Henares |
| Corporate Author |
|
Thesis |
|
| Publisher |
IEEE Xplore |
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OSS2012b |
Serial |
2021 |
| Permanent link to this record |
| |
|
| |
| Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
| Title |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
Type |
Conference Article |
| Year |
2013 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
467 - 472 |
| Keywords |
Pedestrian Detection; Virtual World; Part based |
| 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). |
| Address |
Gold Coast; Australia; June 2013 |
| Corporate Author |
|
Thesis |
|
| Publisher |
IEEE |
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS; 600.054; 600.057 |
Approved |
no |
| Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
| Permanent link to this record |
| |
|
| |
| Author |
Zhijie Fang; Antonio Lopez |
| Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1271 - 1276 |
| Keywords |
|
| Abstract |
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ FaL2018 |
Serial |
3181 |
| Permanent link to this record |
| |
|
| |
| Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
| Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
2176 - 2181 |
| Keywords |
|
| Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). 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, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
|
Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IV |
| Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ GUH2018 |
Serial |
3183 |
| Permanent link to this record |
| |
|
| |
| Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
| Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
IEEE Intelligent Vehicles Symposium IV2015 |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
356-361 |
| Keywords |
Pedestrian Detection |
| Abstract |
Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
| Address |
Seoul; Corea; June 2015 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
ACDC |
Expedition |
|
Conference |
IV |
| Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
| Call Number |
ADAS @ adas @ GVX2015 |
Serial |
2625 |
| Permanent link to this record |
| |
|
| |
| Author |
Fadi Dornaika; Angel Sappa |
| Title |
Real Time on Board Stereo Camera Pose through Image Registration |
Type |
Conference Article |
| Year |
2008 |
Publication |
IEEE Intelligent Vehicles Symposium, |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
804–809 |
| Keywords |
|
| Abstract |
|
| Address |
Eindhoven (Netherlands) |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
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Editor |
|
| Language |
|
Summary Language |
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Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ DoS2008a |
Serial |
1015 |
| Permanent link to this record |
| |
|
| |
| Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
| Title |
Illuminant Invariant Model-Based Road Segmentation |
Type |
Conference Article |
| Year |
2008 |
Publication |
IEEE Intelligent Vehicles Symposium, |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1155–1180 |
| Keywords |
road detection |
| Abstract |
|
| Address |
Eindhoven (The Netherlands) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ADAS;CIC |
Approved |
no |
| Call Number |
ADAS @ adas @ ALB2008 |
Serial |
1045 |
| Permanent link to this record |
| |
|
| |
| Author |
Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
| Title |
Burst Perception-Distortion Tradeoff: Analysis and Evaluation |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
|
| Keywords |
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| Abstract |
Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
CIC; MACO |
Approved |
no |
| Call Number |
Admin @ si @ XHV2023 |
Serial |
3909 |
| Permanent link to this record |
| |
|
| |
| Author |
Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch |
| Title |
Efficient Super-Resolution for Compression Of Gaming Videos |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
|
| Keywords |
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| Abstract |
Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain. |
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Thesis |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
LAMP; MACO |
Approved |
no |
| Call Number |
Admin @ si @ WMH2023 |
Serial |
3911 |
| Permanent link to this record |
| |
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| |
| Author |
Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak |
| Title |
Semantic Preprocessor for Image Compression for Machines |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision. |
| Address |
Rodhes Islands; Greece; June 2023 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICASSP |
| Notes |
MACO; LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YHY2023 |
Serial |
3912 |
| Permanent link to this record |
| |
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| |
| Author |
Lei Kang; Lichao Zhang; Dazhi Jiang |
| Title |
Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods. |
| Address |
Rodhes Islands; Greece; June 2023 |
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Place of Publication |
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Edition |
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Medium |
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Expedition |
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Conference |
ICASSP |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ KZJ2023 |
Serial |
3984 |
| Permanent link to this record |
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| Author |
Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez |
| Title |
You are Fired! Nonverbal Role Analysis in Competitive Meetings |
Type |
Conference Article |
| Year |
2009 |
Publication |
IEEE International Conference on Audio, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1949–1952 |
| Keywords |
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| Abstract |
This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. |
| Address |
Taipei, Taiwan |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1520-6149 |
ISBN |
978-1-4244-2353-8 |
Medium |
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| Area |
|
Expedition |
|
Conference |
ICASSP |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ RVG2009 |
Serial |
1154 |
| Permanent link to this record |
| |
|
| |
| Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate |
| Title |
A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE International Conference on Computer Vision – Workshops |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
2042-2049 |
| Keywords |
IEEE International Conference on Computer Vision – Workshops |
| Abstract |
Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
Barcelona (Spain) |
Editor |
|
| Language |
English |
Summary Language |
English |
Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
|
Conference |
ICCVW |
| Notes |
IAM; ADAS |
Approved |
no |
| Call Number |
IAM @ iam @ MGH2011 |
Serial |
1682 |
| Permanent link to this record |
