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Author Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca edit   pdf
doi  openurl
  Title Human Action Recognition Using an Ensemble of Body-Part Detectors Type Journal Article
  Year 2013 Publication Expert Systems Abbreviated Journal EXSY  
  Volume 30 Issue 2 Pages (up) 101-114  
  Keywords Human action recognition;body-part detection;hidden Markov model  
  Abstract This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods.  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ CBG2013 Serial 1809  
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Author Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva edit   pdf
doi  openurl
  Title On Tracking Inside Groups Type Journal Article
  Year 2010 Publication Machine Vision and Applications Abbreviated Journal MVA  
  Volume 21 Issue 2 Pages (up) 113–127  
  Keywords  
  Abstract This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.  
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  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number ISE @ ise @ RGP2010 Serial 1158  
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Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer edit  doi
openurl 
  Title Generalized Gamut Mapping using Image Derivative Structures for Color Constancy Type Journal Article
  Year 2010 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 86 Issue 2-3 Pages (up) 127-139  
  Keywords  
  Abstract The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut  
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  Publisher Kluwer Academic Publishers Hingham, MA, USA Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number CAT @ cat @ GGW2010 Serial 1274  
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Author Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon edit  url
openurl 
  Title Looking at People Special Issue Type Journal Article
  Year 2018 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 126 Issue 2-4 Pages (up) 141-143  
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  Area Expedition Conference  
  Notes HUPBA; ISE; 600.119 Approved no  
  Call Number Admin @ si @ EGJ2018 Serial 3093  
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Author Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders edit  doi
openurl 
  Title Selective Search for Object Recognition Type Journal Article
  Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 104 Issue 2 Pages (up) 154-171  
  Keywords  
  Abstract This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html).  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ USG2013 Serial 2362  
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