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Hot-Spot Blob Merging for Real-Time Image Segmentation for Privacy Protection

Authors:
Florian Matusek

Keywords: image segmentation; privacy protection; region growing; blob analysis; occlusion; shadow detection; intelligent video surveillance.

Abstract:
One of the major, difficult tasks in automated video surveillance is the segmentation of relevant objects in the scene. This is important for various tracking tasks. Especially in the emerging field of privacy protection in video surveillance systems it is imperative that objects are accurately separated and shadows removed. Current implementations often yield inconsistent results on average from frame to frame when trying to differentiate partly occluding objects. This paper presents an efficient block-based segmentation algorithm, which is capable of separating partly occluding objects and detecting shadows. It has been proven to perform in real-time with a maximum duration of 47.48 ms per frame (for 8x8 blocks on a 720x576 image) with a true positive rate of 89.2%. The flexible structure of the algorithm enables adaptations and improvements with little effort. Most of the parameters correspond to relative differences between quantities extracted from the image and should therefore not depend on scene and lighting conditions. Thus, our proposal is presenting a performance-oriented segmentation algorithm, which is applicable to all critical real-time scenarios.

Pages: 44 to 49

Copyright: Copyright (c) IARIA, 2010

Publication date: October 25, 2010

Published in: conference

ISSN: 2326-9383

ISBN: 978-1-61208-103-8

Location: Florence, Italy

Dates: from October 25, 2010 to October 30, 2010