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检索条件"机构=Computer Vision Center and Computer Science Dpt. UAB"

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Virtual worlds and active learning for human detection 11

Virtual worlds and active learning for human detection

引用

2011 ACM International Conference on Multimodal Interaction, ICMI'11

作者： Vázquez, David López, Antonio M. Ponsa, Daniel Marín, Javier Computer Vision Center Computer Science Dpt. UAB Campus UAB Bellaterra Spain Computer Vision Center Campus UAB Bellaterra Spain

ISBN: (纸本)9781450306416

Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e. trained with labelled samples. However, labelling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e. using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples, which has not been done before. © 2011 ACM.

关键词： Artificial intelligence

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Color spaces emerging from deep convolutional networks 24

Color spaces emerging from deep convolutional networks

引用

24th Color and Imaging Conference: Color science and Engineering Systems, Technologies, and Applications, CIC 2016

作者： Rafegas, Ivet Vanrell, Maria C. Sc. Dpt. UAB Computer Vision Center Bellaterra Barcelona Spain

ISBN: (纸本)9780892083244

Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. © Society for Imaging science and Technology 2016.

关键词： Color

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Articulated Particle Filter for hand tracking

Articulated Particle Filter for hand tracking

引用

21st International Conference on Pattern Recognition, ICPR 2012

作者： Ros, German Del Rincón, Jesús Martinez Mateos, Ginés García Computer Vision Center and Computer Science Dpt. UAB Spain Digital Imaging Research Centre Kingston University London United Kingdom Dpt. Informática y Sistemas University of Murcia Spain

ISBN: (纸本)9784990644109

This paper proposes a new version of Particle Filter, called Articulated Particle Filter - ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. © 2012 ICPR Org Committee.

关键词： Monte Carlo methods

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Color contribution to part-based person detection in different types of scenarios

引用

Lecture Notes in computer science

作者： Rao, Muhammad Anwer Vázquez, David López, Antonio M. Computer Vision Center Computer Science Dpt. Universitat Autònoma de Barcelona 08193 Barcelona Spain

ISBN: (纸本)9783642236778

Camera-based person detection is of paramount interest due to its potential applications. The task is difficult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted sufficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system. We will compute the HOG on top of OPP, then we train and test the part-based human classifier by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the benefits of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. © 2011 Springer-Verlag.

关键词： RGB color model

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Learning photometric invariance from diversified color model ensembles

Learning photometric invariance from diversified color model...

引用

2009 IEEE Conference on computer vision and Pattern Recognition, CVPR 2009

作者： Álvarez, Jose M. Á. Gevers, Theo Ĺopez, Antonio Faculty of Science University of Amsterdam Netherlands Computer Vision Center and Computer Science Dpt. Univ. Autonoma De Barcelona Spain

ISBN: (纸本)9781424439935

Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. ©2009 IEEE.

关键词： Color

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GPU-Accelerated Real-Time Stixel Computation

GPU-Accelerated Real-Time Stixel Computation

引用

IEEE Workshop on Applications of computer vision (WACV)

作者： Daniel Hernandez-Juarez Antonio Espinosa Juan C. Moure David Vázquez Antonio M. López Computer Architecture and Operating Systems Dpt. UAB Computer Vision Center and Computer Science Dpt. UAB

ISBN: (纸本)9781509048236

The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multistixel estimation pipeline on an embedded, energy-efficient, GPU-accelerated device. This work presents a full GPUaccelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.

关键词： Dynamic programming Image segmentation Estimation Computational modeling Real-time systems Optimization Roads

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Learning Appearance in Virtual Scenarios for Pedestrian Detection

Learning Appearance in Virtual Scenarios for Pedestrian Dete...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Javier Marin David Vazquez David Geronimo Antonio M. Lopez Computer Vision Center and Computer Science Dpt. UAB

ISBN: (纸本)9781424469840;9781424469857

Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance.

关键词： Pedestrians Classifiers Virtual training sequence images

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Opponent colors for human detection

Opponent colors for human detection

引用

Lecture Notes in computer science

作者： Muhammad Anwer, Rao Vázquez, David López, Antonio M. Computer Vision Center Computer Science Dpt. Universitat Autònoma de Barcelona 08193 Bellaterra Barcelona Spain

ISBN: (纸本)9783642212567

Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. © 2011 Springer-Verlag.

关键词： Security systems

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Exploiting dynamic aspects of visual perception for object recognition

Exploiting dynamic aspects of visual perception for object r...

引用

1994 From Perception to Action Conference, PerAc 1994

作者： Milanese, R. Pun, T. Gil, S. Bost, J.-M. Computer Vision Group Dpt. Of Computer Science University of Geneva 24 rue du Général Dufour Genève 41211 Switzerland International Computer Science Institute 1947 Center St. BerkeleyCA94704 United States

ISBN: (纸本)0818664827

When computer vision algorithms are applied to autonomous robots, several dynamic aspects of perception must be taken into account. Among the most important ones is the capability to modify the image acquisition parameters, in order to peiform ocular saccades, or to visually track moving objects. This ability is provided by the alerting and attention mechanisms, which allow to rapidly detect and locate potential visual targets. Another important dynamic aspect of perception is the difference in latencies of the neural signals arriving to cells in the visual cortex. Neurophysiological findings suggest for instance that stronger stimuli elicit earlier responses than weaker ones. In data-processing terms, these signals represent a data flow of stimuli. This article describes a computer vision system that exploits these two types of dynamic mechanisms. First, two algorithms are proposed for rapidly detecting interesting parts of the input image;one of them acts on an image sequence, and extracts the regions containing moving objects. The other one acts on a static image, and selects the regions containing the most salient information. The second algorithm performs object recognition by exploiting the different latencies in the data flow of image primitives. The results shown suggest that the proposed mechanisms can be usefully integrated in robotic systems, so as to provide efficient perception-action behaviors. © 1994 IEEE.

关键词： Object recognition

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Illuminant-invariant model-based road segmentation

Illuminant-invariant model-based road segmentation

引用

IEEE Symposium on Intelligent Vehicle

作者： J.M. Alvarez A. Lopez R. Baldrich The Computer Vision Center and Dpt. of Computer Science University Autonoma de Barcelona Barcelona Spain

Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following or vehicle detection and tracking. Significant efforts have been made in order to solve this task using vision-based techniques. One of the major challenges of these techniques is dealing with lighting variations, especially shadows. Many of the approaches presented within this field use ad-hoc mechanisms applied after an initial segmentation to recover shadowed road patches. In this paper, we present an innovative method to obtain a road segmentation algorithm robust to extreme shadow conditions. The novelty of the proposal is the use of a shadowless feature space in combination with a model-based region growing algorithm. The former projects the color images such that the shadow effect is greatly attenuated. The latter uses histogram models to label the pixels as belonging to the road or to the background. These models are constructed on a frame by frame basis independently of the road shape to avoid limitations when addressing unstructured roads. The results presented show the validity of our approach.

关键词： Vehicles

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