DocumentCode :
2916597
Title :
Learning to find occlusion regions
Author :
Humayun, Ahmad ; Mac Aodha, Oisin ; Brostow, Gabriel J.
Author_Institution :
Univ. Coll. London, London, UK
fYear :
2011
fDate :
20-25 June 2011
Firstpage :
2161
Lastpage :
2168
Abstract :
For two consecutive frames in a video, we identify which pixels in the first frame become occluded in the second. Such general-purpose detection of occlusion regions is difficult and important because one-to-one correspondence of imaged scene points is needed for many tracking, video segmentation, and reconstruction algorithms. Our hypothesis is that an effective trained occlusion detector can be generated on the basis of i) a broad spectrum of visual features, and ii) representative but synthetic training sequences. By using a Random Forest based framework for feature selection and training, we found that the proposed feature set was sufficient to frequently assign a high probability of occlusion to just the pixels that were indeed becoming occluded. Our extensive experiments on many sequences support this finding, and while accuracy is certainly still scene-dependent, the proposed classifier could be a useful preprocessing step to exploit temporal information in video.
Keywords :
feature extraction; video signal processing; feature selection; feature training; general-purpose occlusion region detection; imaged scene points; pixels; random forest; video frames; visual features; Computer vision; Image color analysis; Image edge detection; Motion segmentation; Optical imaging; Optical variables control; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4577-0394-2
Type :
conf
DOI :
10.1109/CVPR.2011.5995517
Filename :
5995517
Link To Document :
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