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Author |
David Geronimo; Frederic Lerasle; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
State-driven particle filter for multi-person tracking |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
Abbreviated Journal |
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Volume |
7517 |
Issue |
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Pages |
467-478 |
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Keywords |
human tracking |
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Abstract |
Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences. |
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Brno, Chzech Republic |
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Publisher |
Springer |
Place of Publication |
Heidelberg |
Editor |
J. Blanc-Talon et al. |
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English |
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ACIVS |
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ADAS |
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yes |
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Call Number |
GLL2012; ADAS @ adas @ gll2012a |
Serial |
1990 |
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Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
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Title |
Pedestrian Detection: Exploring Virtual Worlds |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
Abbreviated Journal |
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Volume |
5 |
Issue |
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Pages |
145-162 |
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Keywords |
Virtual worlds; Pedestrian Detection; Domain Adaptation |
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Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
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Publisher |
iConcept Press |
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English |
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978-1-477554-82-1 |
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ADAS |
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no |
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ADAS @ adas @ MGV2012 |
Serial |
1979 |
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Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
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Title |
Improving HOG with Image Segmentation: Application to Human Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
Abbreviated Journal |
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Volume |
7517 |
Issue |
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Pages |
178-189 |
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Keywords |
Segmentation; Pedestrian Detection |
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Abstract |
In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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Address |
Brno, Czech Republic |
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Thesis ![sorted by Thesis field, ascending order (up)](img/sort_asc.gif) |
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Publisher |
Springer Berlin Heidelberg |
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Editor |
J. Blanc-Talon et al. |
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English |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33139-8 |
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ACIVS |
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Notes |
ADAS;ISE |
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no |
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Call Number |
ADAS @ adas @ SLV2012 |
Serial |
1980 |
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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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Thesis ![sorted by Thesis field, ascending order (up)](img/sort_asc.gif) |
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Publisher |
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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Permanent link to this record |
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Author |
David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes |
Type |
Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
Abbreviated Journal |
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Volume |
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Pages |
706 - 711 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. |
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Portland; Oregon; June 2013 |
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Thesis ![sorted by Thesis field, ascending order (up)](img/sort_asc.gif) |
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Publisher |
IEEE |
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English |
Summary Language |
English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
no |
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Call Number |
ADAS @ adas @ VXR2013a |
Serial |
2219 |
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Permanent link to this record |
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Author |
Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers |
Type |
Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
Abbreviated Journal |
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Volume |
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Pages |
688 - 693 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%. |
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Portland; oregon; June 2013 |
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English |
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English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
yes |
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Call Number |
XVR2013; ADAS @ adas @ xvr2013a |
Serial |
2220 |
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Author |
Carles Sanchez |
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Title |
Tracheal ring detection in bronchoscopy |
Type |
Report |
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Year |
2011 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
168 |
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Keywords |
Bronchoscopy, tracheal ring, segmentation |
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Abstract |
Endoscopy is the process in which a camera is introduced inside a human.
Given that endoscopy provides realistic images (in contrast to other modalities) and allows non-invase minimal intervention procedures (which can aid in diagnosis and surgical interventions), its use has spreaded during last decades.
In this project we will focus on bronchoscopic procedures, during which the camera is introduced through the trachea in order to have a diagnostic of the patient. The diagnostic interventions are focused on: degree of stenosis (reduction in tracheal area), prosthesis or early diagnosis of tumors. In the first case, assessment of the luminal area and the calculation of the diameters of the tracheal rings are required. A main limitation is that all the process is done by hand,
which means that the doctor takes all the measurements and decisions just by looking at the screen. As far as we know there is no computational framework for helping the doctors in the diagnosis.
This project will consist of analysing bronchoscopic videos in order to extract useful information for the diagnostic of the degree of stenosis. In particular we will focus on segmentation of the tracheal rings. As a result of this project several strategies (for detecting tracheal rings) had been implemented in order to compare their performance. |
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Thesis ![sorted by Thesis field, ascending order (up)](img/sort_asc.gif) |
Master's thesis |
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Editor |
Debora Gil, F.Javier Sanchez |
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Language |
english |
Summary Language |
english |
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Notes |
IAM;MV |
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no |
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Call Number |
IAM @ iam @ San2011 |
Serial |
1841 |
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Permanent link to this record |
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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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Corporate Author |
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Thesis ![sorted by Thesis field, ascending order (up)](img/sort_asc.gif) |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona |
Editor |
Antonio Lopez;Daniel Ponsa |
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Language |
English |
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ISBN |
978-84-940530-1-6 |
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Notes |
adas |
Approved |
yes |
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Call Number |
ADAS @ adas @ Vaz2013 |
Serial |
2276 |
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Permanent link to this record |