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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez edit  openurl
  Title (up) Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering Type Journal
  Year 2008 Publication Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665 Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MAR2008c Serial 970  
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Author Jordi Gonzalez; Dani Rowe; J. Varona; Xavier Roca edit  doi
openurl 
  Title (up) Understanding Dynamic Scenes based on Human Sequence Evaluation Type Journal Article
  Year 2009 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 27 Issue 10 Pages 1433–1444  
  Keywords Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators  
  Abstract In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages.  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ GRV2009 Serial 1211  
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Author R. Valenti; N. Sebe; Theo Gevers edit  url
doi  openurl
  Title (up) What are you looking at? Improving Visual gaze Estimation by Saliency Type Journal Article
  Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 98 Issue 3 Pages 324-334  
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  Abstract Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36
In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.
 
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  ISSN 0920-5691 ISBN Medium  
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  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ VSG2012 Serial 1848  
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