Training-free moving object detection system based on hierarchical color-guided motion segmentation

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Abstract

We present a moving object detection system for surveillance based on Hierarchical Color-guided Motion segmentation (HiCoMo). The HiCoMo system does not require training and consists of two main stages: (1) hierarchical color-guided motion segmentation, and (2) motion-based verification. The first stage is a hierarchical segmentation framework, where at each level a balance is made between static and temporal features. So that groups of pixels develop into semantic object segments. In the second stage, these object segments are further analyzed in terms of motion saliency and consistency, in order to finalize the object detection results. Our proposed system is tested on real-life surveillance videos containing various scenarios. The detection results outperform a state-of-the-art training-free moving object detection algorithm in recall (90.2% compared to 81.6%) while having a competitively promising precision (96.5% compared to 97.4%). The system has a generic nature and real-time implementation potential, which makes it applicable to various applications of computer vision.

Original languageEnglish
Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages154-157
Number of pages4
ISBN (Electronic)9784901122153
DOIs
Publication statusPublished - 1 Jan 2015
Event14th IAPR International Conference on Machine Vision Applications (MVA 2015) - Tokyo, Japan
Duration: 18 May 201522 May 2015
Conference number: 14
http://www.mva-org.jp/mva2015

Conference

Conference14th IAPR International Conference on Machine Vision Applications (MVA 2015)
Abbreviated titleMVA 2015
CountryJapan
CityTokyo
Period18/05/1522/05/15
Internet address

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