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Author  |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Virtual Worlds and Active Learning for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
393-400 |
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Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
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, labeling 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 (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
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Address |
Alicante, Spain |
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Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
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English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
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978-1-4503-0641-6 |
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ICMI |
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ADAS |
Approved |
yes |
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Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Cool world: domain adaptation of virtual and real worlds for human detection using active learning |
Type |
Conference Article |
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Year |
2011 |
Publication |
NIPS Domain Adaptation Workshop: Theory and Application |
Abbreviated Journal |
NIPS-DA |
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Keywords |
Pedestrian Detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, 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, in Marin et al. we 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 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 and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised 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 use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity. |
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Address |
Granada, Spain |
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Place of Publication |
Granada, Spain |
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English |
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English |
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DA-NIPS |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ VLP2011b |
Serial |
1756 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Interactive Training of Human Detectors |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Multiodal Interaction in Image and Video Applications |
Abbreviated Journal |
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Volume |
48 |
Issue |
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Pages |
169-182 |
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Keywords |
Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation |
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Abstract |
Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. |
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Address |
Springer Heidelberg New York Dordrecht London |
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Springer Berlin Heidelberg |
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English |
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ISSN |
1868-4394 |
ISBN |
978-3-642-35931-6 |
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Notes |
ADAS; 600.057; 600.054; 605.203 |
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no |
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Call Number |
VLP2013; ADAS @ adas @ vlp2013 |
Serial |
2193 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
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Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
3492 - 3495 |
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Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
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Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
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Address |
Tsukuba Science City, Japan |
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Publisher |
IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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Author |
David Vazquez; Antonio Lopez |
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Title |
Intrusion Classification in Intelligent Video Surveillance Systems |
Type |
Report |
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Year |
2008 |
Publication |
Estudis d'Enginyeria Superior en Informática |
Abbreviated Journal |
UAB |
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Keywords |
Human detection; Car detection; Intrusion detection |
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Abstract |
An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system |
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Bellaterra, Spain |
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PFC |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ VL2008a |
Serial |
1670 |
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Author |
David Vazquez |
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Title |
Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
Abbreviated Journal |
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Volume |
1 |
Issue |
1 |
Pages |
1-105 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area. |
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Address |
Barcelona |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona |
Editor |
Antonio Lopez;Daniel Ponsa |
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English |
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978-84-940530-1-6 |
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Notes |
adas |
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yes |
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Call Number |
ADAS @ adas @ Vaz2013 |
Serial |
2276 |
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Author |
David Sanchez-Mendoza; David Masip; Agata Lapedriza |
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Title |
Emotion recognition from mid-level features |
Type |
Journal Article |
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Year |
2015 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
67 |
Issue |
Part 1 |
Pages |
66–74 |
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Keywords |
Facial expression; Emotion recognition; Action units; Computer vision |
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In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. |
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Elsevier B.V. |
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0167-8655 |
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OR;MV |
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Admin @ si @ SML2015 |
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2746 |
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Author |
David Rotger; Petia Radeva; Oriol Rodriguez |
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Title |
Vessel Tortuosity Extraction from IVUS Images |
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Miscellaneous |
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2006 |
Publication |
Computers in Cardiology (CiC´06), 33: 689–692 |
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Valencia (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ RRR2006 |
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762 |
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Author |
David Rotger; Petia Radeva; N. Bruining |
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Title |
Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images using a Cascade of Classifiers |
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Journal Article |
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2010 |
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IEEE Transactions on Information Technology in Biomedicine |
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TITB |
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14 |
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2 |
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535 – 537 |
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Bioabsorbable drug-eluting coronary stents present a very promising improvement to the common metallic ones solving some of the most important problems of stent implantation: the late restenosis. These stents made of poly-L-lactic acid cause a very subtle acoustic shadow (compared to the metallic ones) making difficult the automatic detection and measurements in images. In this paper, we propose a novel approach based on a cascade of GentleBoost classifiers to detect the stent struts using structural features to code the information of the different subregions of the struts. A stochastic gradient descent method is applied to optimize the overall performance of the detector. Validation results of struts detection are very encouraging with an average F-measure of 81%. |
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BCNPCL @ bcnpcl @ RRB2010 |
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1287 |
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Author |
David Rotger; Petia Radeva; J. Mauri; E Fernandez-Nofrerias |
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Title |
Internal and External Coronary Vessel Images Registration. |
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Miscellaneous |
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2002 |
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BCNPCL @ bcnpcl @ RRM2002b |
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318 |
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David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Registering External and Internal Morphological Images of Coronary Vessels. |
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2002 |
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IASTED International Conference Signals Processing, Pattern Recognition and Applications SPPRA. |
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BCNPCL @ bcnpcl @ RRF2002a |
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297 |
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David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Multimodal Registration of Intravascular Ultrasound Images and Angiography. |
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Miscellaneous |
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2002 |
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XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 137–140. |
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Zaragoza, Espanya |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ RRF2002b |
Serial |
317 |
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| Permanent link to this record |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
Abbreviated Journal |
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Volume |
163 |
Issue |
|
Pages |
197–204 |
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Keywords |
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Abstract |
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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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Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-1-58603-798-7 |
Medium |
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Area |
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Expedition |
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Conference |
CCIA’07 |
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Notes |
MILAB |
Approved |
no |
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| |
Call Number |
BCNPCL @ bcnpcl @ RRF2007a |
Serial |
831 |
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| Permanent link to this record |
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| |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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| |
Title |
Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
Computer Analysis of Images and Patterns, 12th International Conference |
Abbreviated Journal |
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Volume |
4673 |
Issue |
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Pages |
285–292 |
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Keywords |
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Abstract |
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Address |
Vienna (Austria) |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-540-74271-5 |
Medium |
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Area |
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Expedition |
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Conference |
CAIP |
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| |
Notes |
MILAB |
Approved |
no |
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| |
Call Number |
BCNPCL @ bcnpcl @ RRF2007b |
Serial |
832 |
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| Permanent link to this record |
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| |
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Author |
David Rotger; Petia Radeva; Cristina Cañero; Juan J. Villanueva; J. Mauri; E Fernandez-Nofrerias; A. Tovar; V. Valle |
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| |
Title |
Corresponding IVUS and Angiogram Image Data |
Type |
Miscellaneous |
| |
Year |
2001 |
Publication |
Proceedings of Computers in Cardiology Rottherdam, (in press). |
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 |
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Address |
The Netherlands |
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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 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 |
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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 |
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| |
Notes |
MILAB |
Approved |
no |
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| |
Call Number |
BCNPCL @ bcnpcl @ RRC2001 |
Serial |
159 |
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| Permanent link to this record |
