Combining ARF and OR-PCA for robust background subtraction of noisy videos

Sajid Javed, Thierry Bouwmans, Soon Ki Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Background subtraction is a fundamental pre-processing step for many computer vision applications. In addition to cope with dynamic background scenes, bad weather conditions such as rainy or snowy environments and global illumination conditions such as light switch on/off are still major challenging problems. Traditional state of the art methods, such as Robust Principal Component Analysis fail to deliver promising results under these worst conditions. This is due to the lack of global preprocessing or post-processing steps, incorrect low-dimensional subspace basis called low-rank matrix estimation, and memory or computational complexities for processing high dimensional data and hence the system does not perform an accurate foreground segmentation. To handle these challenges, this paper presents an input video denoising strategy to cope noisy videos in rainy or snowy conditions. A real time Active Random Field constraint is exploited using probabilistic spatial neighborhood system for image denoising. After that, Online Robust Principal Component Analysis is used to separate the low-rank and sparse component from denoised frames. In addition, a color transfer function is employed between the low-rank and the denoised image for handling abruptly changing lighting conditions, which is a very useful technique for surveillance agents to handle the night time videos. Experimental evaluations, under bad weather conditions using two challenging datasets such as I-LIDS and Change Detection 2014, demonstrate the effectiveness of the proposed method as compared to the existing approaches.

Original languageBritish English
Title of host publicationImage Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
EditorsVittorio Murino, Vittorio Murino, Enrico Puppo
PublisherSpringer Verlag
Pages340-351
Number of pages12
ISBN (Print)9783319232331
DOIs
StatePublished - 2015
Event18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy
Duration: 7 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9280
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Image Analysis and Processing, ICIAP 2015
Country/TerritoryItaly
CityGenoa
Period7/09/1511/09/15

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