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<link rel='alternate' type='application/rss+xml' title='RSS' href='index.xml' />
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Background: #fff Foreground: #000 PrimaryPale: #8cf PrimaryLight: #18f PrimaryMid: #04b PrimaryDark: #014 SecondaryPale: #ffc SecondaryLight: #fe8 SecondaryMid: #db4 SecondaryDark: #841 TertiaryPale: #eee TertiaryLight: #ccc TertiaryMid: #999 TertiaryDark: #666 Error: #f88
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h4 {font-size:1em;}
h5 {font-size:.9em;}
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dt {font-weight:bold;}
ol {list-style-type:decimal;}
ol ol {list-style-type:lower-alpha;}
ol ol ol {list-style-type:lower-roman;}
ol ol ol ol {list-style-type:decimal;}
ol ol ol ol ol {list-style-type:lower-alpha;}
ol ol ol ol ol ol {list-style-type:lower-roman;}
ol ol ol ol ol ol ol {list-style-type:decimal;}
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code.escaped {white-space:nowrap;}
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/* the 'a' is required for IE, otherwise it renders the whole tiddler in bold */
a.tiddlyLinkNonExisting.shadow {font-weight:bold;}
#mainMenu .tiddlyLinkExisting,
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.tabset {padding:1em 0 0 0.5em;}
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.footer li {display:inline;}
.annotation {padding:0.5em; margin:0.5em;}
* html .viewer pre {width:99%; padding:0 0 1em 0;}
.viewer {line-height:1.4em; padding-top:0.5em;}
.viewer .button {margin:0 0.25em; padding:0 0.25em;}
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.zoomer div {padding:1em;}
* html #backstage {width:99%;}
* html #backstageArea {width:99%;}
#backstageArea {display:none; position:relative; overflow: hidden; z-index:150; padding:0.3em 0.5em;}
#backstageToolbar {position:relative;}
#backstageArea a {font-weight:bold; margin-left:0.5em; padding:0.3em 0.5em;}
#backstageButton {display:none; position:absolute; z-index:175; top:0; right:0;}
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#backstagePanel {display:none; z-index:100; position:absolute; width:90%; margin-left:3em; padding:1em;}
.backstagePanelFooter {padding-top:0.2em; float:right;}
.backstagePanelFooter a {padding:0.2em 0.4em;}
#backstageCloak {display:none; z-index:20; position:absolute; width:100%; height:100px;}
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/***
StyleSheet for use when a translation requires any css style changes.
This StyleSheet can be used directly by languages such as Chinese, Japanese and Korean which need larger font sizes.
***/
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body {font-size:0.8em;}
#sidebarOptions {font-size:1.05em;}
#sidebarOptions a {font-style:normal;}
#sidebarOptions .sliderPanel {font-size:0.95em;}
.subtitle {font-size:0.8em;}
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@media print {
#mainMenu, #sidebar, #messageArea, .toolbar, #backstageButton, #backstageArea {display: none !important;}
#displayArea {margin: 1em 1em 0em;}
noscript {display:none;} /* Fixes a feature in Firefox 1.5.0.2 where print preview displays the noscript content */
}
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<!--{{{-->
<div class='header' macro='gradient vert [[ColorPalette::PrimaryLight]] [[ColorPalette::PrimaryMid]]'>
<div class='headerShadow'>
<span class='siteTitle' refresh='content' tiddler='SiteTitle'></span>
<span class='siteSubtitle' refresh='content' tiddler='SiteSubtitle'></span>
</div>
<div class='headerForeground'>
<span class='siteTitle' refresh='content' tiddler='SiteTitle'></span>
<span class='siteSubtitle' refresh='content' tiddler='SiteSubtitle'></span>
</div>
</div>
<div id='mainMenu' refresh='content' tiddler='MainMenu'></div>
<div id='sidebar'>
<div id='sidebarOptions' refresh='content' tiddler='SideBarOptions'></div>
<div id='sidebarTabs' refresh='content' force='true' tiddler='SideBarTabs'></div>
</div>
<div id='displayArea'>
<div id='messageArea'></div>
<div id='tiddlerDisplay'></div>
</div>
<!--}}}-->
<!--{{{-->
<div class='toolbar' macro='toolbar [[ToolbarCommands::ViewToolbar]]'></div>
<div class='title' macro='view title'></div>
<div class='subtitle'><span macro='view modifier link'></span>, <span macro='view modified date'></span> (<span macro='message views.wikified.createdPrompt'></span> <span macro='view created date'></span>)</div>
<div class='tagging' macro='tagging'></div>
<div class='tagged' macro='tags'></div>
<div class='viewer' macro='view text wikified'></div>
<div class='tagClear'></div>
<!--}}}-->
<!--{{{-->
<div class='toolbar' macro='toolbar [[ToolbarCommands::EditToolbar]]'></div>
<div class='title' macro='view title'></div>
<div class='editor' macro='edit title'></div>
<div macro='annotations'></div>
<div class='editor' macro='edit text'></div>
<div class='editor' macro='edit tags'></div><div class='editorFooter'><span macro='message views.editor.tagPrompt'></span><span macro='tagChooser excludeLists'></span></div>
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To get started with this blank [[TiddlyWiki]], you'll need to modify the following tiddlers: * [[SiteTitle]] & [[SiteSubtitle]]: The title and subtitle of the site, as shown above (after saving, they will also appear in the browser title bar) * [[MainMenu]]: The menu (usually on the left) * [[DefaultTiddlers]]: Contains the names of the tiddlers that you want to appear when the TiddlyWiki is opened You'll also need to enter your username for signing your edits: <<option txtUserName>>
These [[InterfaceOptions]] for customising [[TiddlyWiki]] are saved in your browser Your username for signing your edits. Write it as a [[WikiWord]] (eg [[JoeBloggs]]) <<option txtUserName>> <<option chkSaveBackups>> [[SaveBackups]] <<option chkAutoSave>> [[AutoSave]] <<option chkRegExpSearch>> [[RegExpSearch]] <<option chkCaseSensitiveSearch>> [[CaseSensitiveSearch]] <<option chkAnimate>> [[EnableAnimations]] ---- Also see [[AdvancedOptions]]
<<importTiddlers>>
I received the Telecommunication Engineer degree in 2006 from the TECNUN School of the Universidad de Navarra, in San Sebastián, Spain, and the Ph.D. degree with summa cum laude in 2012, from the E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid (UPM), Spain. [>img[Jon Arróspide|http://www.gti.ssr.upm.es/~jal/BioPhoto/Foto_web.png]] I worked at the Fraunhofer Institut für Integrierte Schaltungen in Erlangen (Germany) in 2006-2007. Since 2007, I am a member of the Grupo de Tratamiento de Imágenes (GTI, [[Image Processing Group|http://www.gti.ssr.upm.es/]]) of the UPM, where I am currently a post-doctoral fellow researcher. I also obtained a M.Sc. in Communication Technologies and Systems from the UPM in 2009. My current research focuses on image and video analysis for object detection and tracking, especially related to intelligent transport systems.
[[Carlos Roberto del Blanco|http://www.gti.ssr.upm.es/~cda]] [[Carlos Cuevas|http://www.gti.ssr.upm.es/~ccr/]] [[Marcos Nieto|http://marcosnieto.zymichost.com/]] [[Guillermo Gallego|http://www.gti.ssr.upm.es/~ggb/]]
''Jon Arróspide Laborda'' Grupo de Tratamiento de Imágenes/Image Processing Group (GTI) Departamento de Señales, Sistemas y Radicomunicaciones/Signal, System and Radiocommunication Department E.T.S.I. Telecomunicación/ Telecommunication Engineering School Universidad Politécnica de Madrid/Polytechnic University of Madrid Avenida Complutense 30, 28040 Madrid (Spain) email: jal@gti.ssr.upm.es Phone: +(34) 913367353
[[About me]]
Object detection and tracking is one of the main research lines related to computer vision within the Grupo de Tratamiento de Imágenes. Numerous works have been published in the field by the group in high impact international conferences and journals (see [[Publications|http://www.gti.ssr.upm.es/es/publicaciones/internacionales.html]]). This work, named ''~HOMOGRAPHY-BASED GROUND PLANE DETECTION USING A SINGLE ~ON-BOARD CAMERA'' was published in IET Intelligent Transport Systems journal in 2010. The work is based on the previous detection of the ground plane through planar homography estimation between successive frames. The detection of the ground plane allows a further detection of the elements on the ground plane. The method has been applied to vehicle detection and tracking in traffic scenarios with a vehicle-mounted camera. Videos showing the results of the method for two example sequences are enclosed below: Example 1 |<html><a href=./videos/IET/Example11.avi target="_blank"><img src="./videos/IET/Example11.jpg"></a> </html>|<html><a href=./videos/IET/Example12.avi target="_blank"><img src="./videos/IET/Example12.jpg"></a> </html>| |!Video 1.1|!Video 1.2|f Example 2 |<html><a href=./videos/IET/Example21.avi target="_blank"><img src="./videos/IET/Example21.jpg"></a> </html>|<html><a href=./videos/IET/Example22.avi target="_blank"><img src="./videos/IET/Example22.jpg"></a> </html>|<html><a href=./videos/IET/Example23.avi target="_blank"><img src="./videos/IET/Example23.jpg"></a> </html>| |!Video 2.1|!Video 2.2|!Video2.3|f Videos 1.1 and 2.1 show the ground plane detection results for the examples 1 and 2, respectively. The videos comprise the input sequence with a green mask over the detected ground plane at each instant. Videos 1.2 and 2.2 show the vehicle detection results for the same examples. Detection of vehicles is represented by bounding boxes surrounding the rear of the vehicles. The region of interest comprises the own and the two adjacent lanes and is longitudinally bounded by a red line, which indicates the region in which the resolution of the original image is sufficient to obtain reliable information. In addition, for the example 2 a video containing the ground-truth has been generated using a semi-automatic ground-truth generation tool. This video is available (video 2.3) for all the researchers that want to use it as a benchmark.
[[About me]] [[Publications]] [[Vehicle Image Database]] [[Colleagues' websites]] [[Contact]]
''Journals'' __2012__ ''J. Arróspide'', L. Salgado, M. Nieto, “On-road visual vehicle tracking using MCMC with Metropolis sampling”, International Journal of Automotive Technology (in press) ''J. Arróspide'', L. Salgado, M. Nieto, “Video analysis based vehicle detection and tracking using an MCMC sampling framework”, EURASIP Journal on Advances in Signal Processing, vol. 2012, Article ID 2012:2, Jan. 2012. (doi: 10.1186/1687-6180-2012-2) [[[pdf|http://asp.eurasipjournals.com/content/2012/1/2/abstract]]] __2011__ M. Nieto, ''J. Arróspide'', L. Salgado, "Road environment modeling using robust perspective analysis and recursive Bayesian segmentation", Machine Vision and Applications, vol. 22, no. 6, pp. 927-945, Nov. 2011. (DOI 10.1007/s00138-010-0287-7). [[[pdf|http://www.springerlink.com/content/34478581q1850559/fulltext.pdf]]] __2010__ ''J. Arróspide'', L. Salgado, M. Nieto, R. Mohedano, "//[[Homography-based ground plane detection using a single on-board camera|Ground Plane Detection]]//", IET Intelligent Transport Systems, vol. 4, no. 2, pp. 149-160, Jun. 2010. (DOI: 10.1049/iet-its.2009.0073) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05478407&tag=1]]] ''International Conferences'' __2012__ ''J. Arróspide'', L. Salgado, “Region-dependent vehicle classification using PCA features”, IEEE Int. Conf. on Image Processing, ICIP 2012, Orlando (FL), USA, 30 Sep. - 3 Oct. 2012 (in press). J. Marinas, L. Salgado, ''J. Arróspide'', M. Camplani, “Traffic sign detection and tracking using robust 3D Analysis”. Int. Conf. on Emerging Security Technologies, EST 2012, Lisbon, Portugal, 5-7 Sept. 2012 (in press). M. Pérez, L. Salgado, ''J. Arróspide'', J. Marinas, M. Nieto, "Multi-camera very wide baseline feature matching based on view-adaptive junction detection", Int. Conf. on Emerging Security Technologies, EST 2012, Lisbon, Portugal, 5-7 Sept. 2012 (in press). ''J. Arróspide'', L. Salgado, J. Marinas, “~HOG-like gradient-based descriptor for visual vehicle detection”, IEEE Intelligent Vehicles Symposium, IV 2012, Alcalá de Henares, Spain, pp. 223-228, 3-7 Jun. 2012. (DOI 10.1109/IVS.2012.6232119) [[[pdf|http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6232119&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6232109%29]]] J. Marinas, L. Salgado, ''J. Arróspide'', M. Camplani, “Efficient hybrid monocular-stereo approach to on-board, video-based traffic sign detection and tracking”, IS&T/SPIE Int. Conf. on Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, San Francisco Airport (CA), USA, SPIE vol. 8301, pp. 1-15, 23-24 Jan. 2012. (doi: 10.1117/12.908585) __2011__ J. Marinas, L. Salgado, ''J. Arróspide'', M. Nieto, “Detection and tracking of traffic signs using a recursive Bayesian decision framework”, IEEE Annual Conf. on Intelligent Transportation Systems, ITSC 2011, Washington (DC), USA, pp. 1942-1947, 5-7 Oct. 2011. (DOI: 10.1109/ITSC.2011.6082905) [[[pdf|http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6082905&contentType=Conference+Publications&searchField%3DSearch_All%26queryText%3DDetection+and+tracking+of+traffic+signs+using+a+recursive+Bayesian+decision+framework]]] __2010__ ''J. Arróspide'', L. Salgado, M. Nieto, “Multiple object tracking using an automatic variable-dimension particle filter”, IEEE Int. Conf. on Image Processing, ICIP 2010, ~Hong-Kong, China, pp. 49-52, 26-29 Sep. 2010. (DOI 10.1109/ICIP.2010.5651632) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5651632]]] ''J. Arróspide'', L. Salgado, M. Nieto, "Vehicle detection and tracking using homography-based plane rectification and particle filtering", IEEE Intelligent Vehicles Symposium, IV 2010, San Diego (CA), USA, pp. 150-155, 21-24 Jun. 2010. (DOI 10.1109/IVS.2010.5547980) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05547980]]] __2009__ ''J. Arróspide'', L. Salgado, M. Nieto, F. Jaureguizar, “Real-time vehicle detection and tracking based on perspective and non-perspective space cooperation”, IS&T/SPIE Int. Conf. on ~Real-Time Image and Video Processing, San Jose (CA), USA, SPIE vol. 7244, pp. 72440H-1-12, 18-22 Jan 2009. (DOI 10.1117/12.812253) __2008__ M. Nieto, ''J. Arróspide'', L. Salgado, F. Jaureguizar, “Robust multiple lane road modeling based on perspective analysis”, IEEE Int. Conf. on Image Processing, ICIP 2008, San Diego (CA), USA, pp. 2396-2399, 12-15 Oct. 2008. (DOI 10.1109/ICIP.2008.4712275) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04712275]]] ''J. Arróspide'', L. Salgado, M. Nieto, F. Jaureguizar, “On-board robust multiple vehicle detection and tracking using adaptive quality evaluation”, IEEE Int. Conf. on Image Processing, ICIP 2008, San Diego (CA), USA, pp. 2008-2011, 12-15 Oct. 2008. (DOI 10.1109/ICIP.2008.4712178) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4712178]]] M. Nieto, ''J. Arróspide'', L. Salgado, F. Jaureguizar, “~On-board video based system for robust road modeling”, IEEE Int. Workshop ~Content-Based Multimedia Indexing, CBMI 2008, London, UK, pp. 109-116, 18-20 Jun. 2008. (DOI 10.1109/CBMI.2008.4564935) [[[pdf|http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4564935]]]
Personal Research Profile
Jon Arróspide
The [[Image Processing Group|http://www.gti.ssr.upm.es/]] is currently researching on the vision-based vehicle classification task. In order to evaluate our methods, we have created a new Database of images that we have extracted from our video sequences (acquired with a forward looking camera mounted on a vehicle). The database comprises 3425 ''images of vehicle rears'' taken from different points of view, and 3900 images extracted from road sequences not containing vehicles. Images are selected to maximize the representativity of the vehicle class, which involves a naturally high variability. In our opinion one important feature affecting the appearance of the vehicle rear is the position of the vehicle relative to the camera. Therefore, the database separates images in four different regions according to the pose: middle/close range in front of the camera, middle/close range in the left, close/middle range in the right, and far range. In addition, the images are extracted in such a way that they do not perfectly fit the contour of the vehicle in order to make the classifier more robust to offsets in the hypothesis generation stage. Instead, some images contain the vehicle loosely (some background is also included in the image), while others only contain the vehicle partially. Several instances of a same vehicle are included with different bounding hypotheses. The images have 64x64 and are cropped from sequences of 360x256 pixels recorded in highways of Madrid, Brussels and Turin. The database is open for use of other researchers and can be downloaded <html><a href="download.html" target ="_blank">here </a> </html>. [img[Middle/Close-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Close/image1.png]][img[Middle/Close-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Close/image2.png]][img[Middle/Close-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Close/image3.png]][img[Middle/Close-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Close/image4.png]][img[Middle/Close-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Close/image5.png]] [img[Left view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Left/image1.png]][img[Left view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Left/image2.png]][img[Left view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Left/image3.png]][img[Left view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Left/image4.png]][img[Left view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Left/image5.png]] [img[Right view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Right/image1.png]][img[Right view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Right/image2.png]][img[Right view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Right/image3.png]][img[Right view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Right/image4.png]][img[Right view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Right/image5.png]] [img[Far-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Far/image1.png]][img[Far-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Far/image2.png]][img[Far-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Far/image3.png]][img[Far-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Far/image4.png]][img[Far-range view|http://www.gti.ssr.upm.es/~jal/ExampleImages/Far/image5.png]] Apart from the images of our own collection, we are also using a small set of images of other databases in order to round the number of images to 4000 thousand vehicles images and 4000 non-vehicle images. In particular, we are using images extracted from the [[Caltech Database|http://www.vision.caltech.edu/html-files/archive.html]] [1][2] and the [[TU Graz-02 Database|http://www.emt.tugraz.at/~pinz/data/GRAZ_02/]] [3][4]. The subset of images from these databases used for our experiments images is linked [[here|http://www.gti.ssr.upm.es/~jal/Database/CaltechGraz.rar]]. The complete set of images is selected so that it covers many different drivind conditions, especially relating to weather. In fact, from the 2000 images devoted to each of the image regions (1000 vehicles instances and 1000 non-vehicle images), 20% of them are taken in sunny weather, 20% in cloudy days, 20% in medium conditions (neither very sunny nor cloudy), 20% with poor illumination (down/dusk), 10% with light rain, 5% with bad resolution cameras, and 2,5% in tunnels (with artificial light). [1] The Caltech Database (Computational Vision at California Institute of Technology, Pasadena), http://www.vision.caltech.edu/html-files/archive.html. Accessed 14 May 2011. [2] R Fergus, P Perona, A Zisserman, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, 16–22 June 2003. [3] The TU Graz-02 Database (Graz University of Technology), http://www.emt.tugraz.at/~pinz/data/GRAZ_02/. Accessed 14 May 2011. [4] A Opelt, A Pinz, in Proceedings of the 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, 19–22 June 2005.