DocumentCode :
1942030
Title :
Unsupervised video leap segmentation for fast detection of salient segment transformations in mobile sequences
Author :
Forsthoefel, Dana ; Wills, D. Scott ; Wills, Linda M.
Author_Institution :
Sch. of ECE, Georgia Tech, Atlanta, GA, USA
fYear :
2012
fDate :
16-19 Sept. 2012
Firstpage :
728
Lastpage :
733
Abstract :
Multiple-frame segmentation, also referred to as video segmentation, is an important step in many video analysis applications for identifying and tracking specific features as they move through a scene. In a mobile, resource-constrained environment such as an intelligent vehicle system, video segmentation can be utilized in preprocessing to reduce image information and increase processing efficiency for high-level scene understanding applications. We introduce video leap segmentation, a highly efficient multiple-frame segmentation approach for use on embedded and mobile platforms where processing speed is critical. The proposed method is demonstrated to successfully track segments across spatial and temporal bounds, generating fast, stable segmentations of images from captured moving-camera video sequences. Video leap segmentation is applied to the task of rough salient segment transformation detection for alerting potential drivers of critical scene changes that may affect steering decisions. Trial results demonstrate that with little added computation, video leap segmentation can be utilized for salient region detection in traffic scenes with high accuracy, correctly detecting 80% of salient segment transformations in trial scenes with less than 5% false positives. Reducing high-level processing to salient areas using the proposed approach has the potential to significantly improve the processing efficiency of scene interpretation applications in intelligent vehicle systems.
Keywords :
image segmentation; image sequences; road traffic; video cameras; video signal processing; captured moving-camera video sequences; high-level processing reduction; image information reduction; intelligent vehicle system; mobile sequences; multiple-frame segmentation approach; resource-constrained environment; rough salient segment transformation detection; salient region detection; salient segment transformations; spatial bounds; temporal bounds; traffic scenes; unsupervised video leap segmentation; video analysis; Accuracy; Image color analysis; Image segmentation; Mobile communication; Tiles; Vectors; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
2153-0009
Print_ISBN :
978-1-4673-3064-0
Electronic_ISBN :
2153-0009
Type :
conf
DOI :
10.1109/ITSC.2012.6338780
Filename :
6338780
Link To Document :
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