Vanishing point-based image transforms for enhancement of probabilistic occupancy map-based people localization

The widespread use of vision-based surveillance systems has inspired many research efforts on people localization. In this paper, a series of novel image transforms based on the vanishing point of vertical lines is proposed for enhancement of the probabilistic occupancy map (POM)-based people locali...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 12 vom: 03. Dez., Seite 5586-98
1. Verfasser: Lin, Yen-Shuo (VerfasserIn)
Weitere Verfasser: Lo, Kuo-Hua, Chen, Hua-Tsung, Chuang, Jen-Hui
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a The widespread use of vision-based surveillance systems has inspired many research efforts on people localization. In this paper, a series of novel image transforms based on the vanishing point of vertical lines is proposed for enhancement of the probabilistic occupancy map (POM)-based people localization scheme. Utilizing the characteristic that the extensions of vertical lines intersect at a vanishing point, the proposed transforms, based on image or ground plane coordinate system, aims at producing transformed images wherein each standing/walking person will have an upright appearance. Thus, the degradation in localization accuracy due to the deviation of camera configuration constraint specified can be alleviated, while the computation efficiency resulted from the applicability of integral image can be retained. Experimental results show that significant improvement in POM-based people localization for more general camera configurations can indeed be achieved with the proposed image transforms 
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650 4 |a Research Support, Non-U.S. Gov't 
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700 1 |a Chen, Hua-Tsung  |e verfasserin  |4 aut 
700 1 |a Chuang, Jen-Hui  |e verfasserin  |4 aut 
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