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Author Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera
Title GrabCut-Based Human Segmentation in Video Sequences Type Journal Article
Year 2012 Publication Sensors Abbreviated Journal SENS
Volume 12 Issue 11 Pages (down) 15376-15393
Keywords segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field
Abstract In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.
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Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ HRP2012 Serial 2147
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Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu
Title Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors Type Journal Article
Year 2012 Publication Sensors Abbreviated Journal SENS
Volume 12 Issue 10 Pages (down) 13333-13348
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Abstract In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection.
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Notes OR;MV Approved no
Call Number Admin @ si @ ISR2012b Serial 2173
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Author Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo
Title Multispectral Image Feature Points Type Journal Article
Year 2012 Publication Sensors Abbreviated Journal SENS
Volume 12 Issue 9 Pages (down) 12661-12672
Keywords multispectral image descriptor; color and infrared images; feature point descriptor
Abstract Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.
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Notes ADAS Approved no
Call Number Admin @ si @ ABL2012 Serial 2154
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Author Albert Ali Salah; E. Pauwels; R. Tavenard; Theo Gevers
Title T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data Type Journal Article
Year 2010 Publication Sensors Abbreviated Journal SENS
Volume 10 Issue 8 Pages (down) 7496-7513
Keywords sensor networks; temporal pattern extraction; T-patterns; Lempel-Ziv; Gaussian mixture model; MERL motion data
Abstract The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.
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Notes ALTRES;ISE Approved no
Call Number Admin @ si @ SPT2010 Serial 1845
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Author Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez
Title SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities Type Journal Article
Year 2020 Publication Sensors Abbreviated Journal SENS
Volume 20 Issue 16 Pages (down) 4587
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Abstract This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.
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Notes MSIAU; 600.130; 601.349; 600.122 Approved no
Call Number Admin @ si @ MSM2020 Serial 3452
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Author Idoia Ruiz; Joan Serrat
Title Hierarchical Novelty Detection for Traffic Sign Recognition Type Journal Article
Year 2022 Publication Sensors Abbreviated Journal SENS
Volume 22 Issue 12 Pages (down) 4389
Keywords Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision
Abstract Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.
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Notes ADAS; 600.154 Approved no
Call Number Admin @ si @ RuS2022 Serial 3684
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Author Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez
Title A survey on model based approaches for 2D and 3D visual human pose recovery Type Journal Article
Year 2014 Publication Sensors Abbreviated Journal SENS
Volume 14 Issue 3 Pages (down) 4189-4210
Keywords human pose recovery; human body modelling; behavior analysis; computer vision
Abstract Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature.
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Notes HuPBA; ISE; 600.046; 600.063; 600.078;MILAB Approved no
Call Number Admin @ si @ PEA2014 Serial 2443
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Author P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa
Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
Year 2014 Publication Sensors Abbreviated Journal SENS
Volume 14 Issue 2 Pages (down) 3690-3701
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Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
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Notes ADAS;600.055; 600.076 Approved no
Call Number Admin @ si @ RCA2014a Serial 2474
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Author Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa
Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
Year 2020 Publication Sensors Abbreviated Journal SENS
Volume 20 Issue 11 Pages (down) 3249
Keywords stereo matching; deep learning; embedded GPU
Abstract Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices.
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Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ AAN2020 Serial 3428
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Author Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate
Title Weather Classification by Utilizing Synthetic Data Type Journal Article
Year 2022 Publication Sensors Abbreviated Journal SENS
Volume 22 Issue 9 Pages (down) 3193
Keywords Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems
Abstract Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.
Address 21 April 2022
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Publisher MDPI Place of Publication Editor
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Notes IAM; 600.139; 600.159; 600.166; 600.145; Approved no
Call Number Admin @ si @ MKE2022 Serial 3761
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Author Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez
Title Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches Type Journal Article
Year 2021 Publication Sensors Abbreviated Journal SENS
Volume 21 Issue 9 Pages (down) 3185
Keywords co-training; multi-modality; vision-based object detection; ADAS; self-driving
Abstract Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ GVL2021 Serial 3562
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution Type Journal Article
Year 2022 Publication Sensors Abbreviated Journal SENS
Volume 22 Issue 6 Pages (down) 2254
Keywords Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images
Abstract This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.
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Notes MSIAU; Approved no
Call Number Admin @ si @ RSV2022b Serial 3688
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Author Zhijie Fang; David Vazquez; Antonio Lopez
Title On-Board Detection of Pedestrian Intentions Type Journal Article
Year 2017 Publication Sensors Abbreviated Journal SENS
Volume 17 Issue 10 Pages (down) 2193
Keywords pedestrian intention; ADAS; self-driving
Abstract Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information.
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Notes ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 Approved no
Call Number Admin @ si @ FVL2017 Serial 2983
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Author Xavier Soria; Angel Sappa; Riad I. Hammoud
Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
Year 2018 Publication Sensors Abbreviated Journal SENS
Volume 18 Issue 7 Pages (down) 2059
Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs
Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Notes ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no
Call Number Admin @ si @ SSH2018 Serial 3145
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Author Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah
Title Human Pose Estimation from Monocular Images: A Comprehensive Survey Type Journal Article
Year 2016 Publication Sensors Abbreviated Journal SENS
Volume 16 Issue 12 Pages (down) 1966
Keywords human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods
Abstract Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
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Notes ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ GZG2016 Serial 2933
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