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Generic fusion of visual cues applied to real-world object segmentation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2005 (English)In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-4, 2005, 2954-2959 p.Conference paper (Refereed)
Abstract [en]

Fusion of information from different complementary sources may be necessary to achieve a robust sensing system that degrades gracefully under various conditions. Many approaches use a specific tailor-made combination of algorithms that do not easily allow the inclusion of more, or other, types of algorithms. In this paper, we explore a variant of a generic algorithm for fusing visual cues to the task of object segmentation in a video stream. The fusion algorithm combines the output of several segmentation algorithms in a straight forward way by using a bayesian approach and a particle filter to track several hypotheses. Segmentation algorithms can be added or removed without changing the over all structure of the system. It was or particular interest to investigate if the method was suitable when realistic real-world scenes with much noise was analysed. The system has been tested on image sequences taken from a moving vehicle where stationary and moving objects are successfully segmented from the background. In conclusion, the fusion algorithm explored is well suited to this problem domain and is easily adopted. The context of this work is on-line pedestrian detection to be deployed in cars.

Place, publisher, year, edition, pages
2005. 2954-2959 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-43370DOI: 10.1109/IROS.2005.1545425ISI: 000235632102146ScopusID: 2-s2.0-79958017864ISBN: 0-7803-8912-3OAI: diva2:448175
IEEE/RSJ International Conference on Intelligent Robots and Systems Location: Edmonton, CANADA Date: AUG 02-06, 2005
QC 20111014Available from: 2011-10-14 Created: 2011-10-14 Last updated: 2011-10-14Bibliographically approved

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Arnell, Fredrik
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Computer Vision and Active Perception, CVAP
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