Leveraging Colour Segmentation for Upper-Body Detection
| Type of publication: | Journal paper |
| Citation: | Duffner_PR_2014 |
| Publication status: | Published |
| Journal: | Pattern Recognition |
| Volume: | 47 |
| Number: | 6 |
| Year: | 2014 |
| Pages: | 2222-2230 |
| Abstract: | This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation maps are then classified as true or false upper-bodies. By further fusing the score of this latter classifier with the base detection score, the method shows a performance improvement on three different public datasets and using two different upper-body base detectors, demonstrating the complementarity of the contextual semantic colour segmentation and the base detector. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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