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Maria Ines Torres; Javier Mikel Olaso; Cesar Montenegro; Riberto Santana; A.Vazquez; Raquel Justo; J.A.Lozano; Stephan Schogl; Gerard Chollet; Nazim Dugan; M.Irvine; N.Glackin; C.Pickard; Anna Esposito; Gennaro Cordasco; Alda Troncone; Dijana Petrovska Delacretaz; Aymen Mtibaa; Mohamed Amine Hmani; M.S.Korsnes; L.J.Martinussen; Sergio Escalera; C.Palmero Cantariño; Olivier Deroo; O.Gordeeva; Jofre Tenorio Laranga; E.Gonzalez Fraile; Begoña Fernandez Ruanova; A.Gonzalez Pinto |
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Title |
The EMPATHIC project: mid-term achievements |
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Conference Article |
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Year |
2019 |
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12th ACM International Conference on PErvasive Technologies Related to Assistive Environments |
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629-638 |
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Maria Ines Torres; Javier Mikel Olaso, César Montenegro, Riberto Santana, A. Vázquez, Raquel Justo, J. A. Lozano, Stephan Schlögl, Gérard Chollet, Nazim Dugan, M. Irvine, N. Glackin, C. Pickard, Anna Esposito, Gennaro Cordasco, Alda Troncone, Dijana Petrovska-Delacrétaz, Aymen Mtibaa, Mohamed Amine Hmani, M. S. Korsnes, L. J. Martinussen, Sergio Escalera, C. Palmero Cantariño, Olivier Deroo, O. Gordeeva, Jofre Tenorio-Laranga, E. Gonzalez-Fraile, Begoña Fernández-Ruanova, A. Gonzalez-Pinto |
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Rhodes Greece; June 2019 |
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PETRA |
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HUPBA; no proj |
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no |
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Admin @ si @ TOM2019 |
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3325 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Title |
Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
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Journal Article |
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Year |
2016 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
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Volume |
9 |
Issue |
2 |
Pages |
161-175 |
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Keywords |
Mirroring; Nodding; Competence; Perception; Wearable Technology |
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Abstract |
Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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OR; 600.072;MV |
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no |
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Admin @ si @ MTR2016 |
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2826 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Title |
A Social-Aware Assistant to support individuals with visual impairments during social interaction: A systematic requirements analysis |
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Journal Article |
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Year |
2019 |
Publication |
International Journal of Human-Computer Studies |
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IJHC |
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Volume |
122 |
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Pages |
50-60 |
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Visual impairment affects the normal course of activities in everyday life including mobility, education, employment, and social interaction. Most of the existing technical solutions devoted to empowering the visually impaired people are in the areas of navigation (obstacle avoidance), access to printed information and object recognition. Less effort has been dedicated so far in developing solutions to support social interactions. In this paper, we introduce a Social-Aware Assistant (SAA) that provides visually impaired people with cues to enhance their face-to-face conversations. The system consists of a perceptive component (represented by smartglasses with an embedded video camera) and a feedback component (represented by a haptic belt). When the vision system detects a head nodding, the belt vibrates, thus suggesting the user to replicate (mirror) the gesture. In our experiments, sighted persons interacted with blind people wearing the SAA. We instructed the former to mirror the noddings according to the vibratory signal, while the latter interacted naturally. After the face-to-face conversation, the participants had an interview to express their experience regarding the use of this new technological assistant. With the data collected during the experiment, we have assessed quantitatively and qualitatively the device usefulness and user satisfaction. |
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Notes |
LAMP; 600.109; 600.120 |
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no |
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Call Number |
Admin @ si @ MTR2019 |
Serial |
3142 |
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Author |
Maria del Camp Davesa |
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Title |
Human action categorization in image sequences |
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Report |
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Year |
2011 |
Publication |
CVC Technical Report |
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Volume |
169 |
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Address |
Bellaterra (Spain) |
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Computer Vision Center |
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Master's thesis |
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CiC;CIC |
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no |
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Admin @ si @ Dav2011 |
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1934 |
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Author |
Maria Alberich-Carramiñana; Guillem Alenya; Juan Andrade; E. Martinez; Carme Torras |
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Title |
Affine Epipolar Direction from Two Views of a Planar Contour |
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2006 |
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Proceedings of the Advanced Concepts for Intelligent Vision Systems Conference, LNCS 4179: 944–955 |
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Antwerp (Belgium) |
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no |
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Admin @ si @ AAA2006 |
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661 |
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Author |
Margarita Torre; Petia Radeva |
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Title |
Agricultural-Field Extraction on Aerial Images by Region Competition Algorithm |
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Conference Article |
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Year |
2000 |
Publication |
15 th International Conference on Pattern Recognition |
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1 |
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313-316 |
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Barcelona, Spain, 2000 |
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ICPR |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ Tor2000a |
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222 |
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Author |
Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez |
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Title |
DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation |
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Journal Article |
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Year |
2020 |
Publication |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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JSTAEOR |
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13 |
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726-737 |
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One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. |
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MILAB |
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no |
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Admin @ si @ TRR2020 |
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3410 |
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Author |
Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte |
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Title |
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement |
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Conference Article |
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2024 |
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38th AAAI Conference on Artificial Intelligence |
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3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. |
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AAAI |
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CIC; MACO |
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no |
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Admin @ si @ CVB2024 |
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3872 |
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Author |
Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
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Title |
Perceptual image enhancement for smartphone real-time applications |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision |
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1848-1858 |
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Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones. |
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Waikoloa; Hawai; USA; January 2023 |
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WACV |
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MACO; CIC |
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no |
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Admin @ si @ CVV2023 |
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3900 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca |
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Toward Real-Time Pedestrian Detection Based on a Deformable Template Model |
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Journal Article |
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2014 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
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1 |
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355-364 |
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Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. |
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1524-9050 |
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ISE; 601.213; 600.078 |
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PGH2014 |
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2350 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Juan J. Villanueva |
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Title |
High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients |
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Conference Article |
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2009 |
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4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one.
Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated.
Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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ISE |
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ISE @ ise @ PGV2009 |
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1214 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca |
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Title |
Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications |
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Journal Article |
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2011 |
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Pattern Recognition Letters |
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PRL |
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32 |
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13 |
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1581-1587 |
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Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.
This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.
In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster. |
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ISE |
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Admin @ si @ PGB2011a |
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1707 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva |
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Recursive Coarse-to-Fine Localization for fast Object Recognition |
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Conference Article |
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2010 |
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11th European Conference on Computer Vision |
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6313 |
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II |
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280–293 |
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Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter. |
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Crete (Greece) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-15566-6 |
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ECCV |
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DAG @ dag @ PGB2010 |
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1438 |
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Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca |
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A coarse-to-fine approach for fast deformable object detection |
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Journal Article |
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2015 |
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Pattern Recognition |
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48 |
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5 |
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1844-1853 |
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We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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ISE; 600.078; 602.005; 605.001; 302.012 |
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Admin @ si @ PVG2015 |
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2628 |
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Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez |
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A Coarse-to-fine Approach for fast Deformable Object Detection |
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2011 |
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IEEE conference on Computer Vision and Pattern Recognition |
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1353-1360 |
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Colorado Springs; USA |
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Admin @ si @ PVG2011 |
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1764 |
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