Open AccessReal-time performance evaluation of BGSLibrary algorithms for intelligent surveillance
Author(s) -
Imane Benraya,
Nadjia Benblidia,
Yasmine Amara
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i3.pp1491-1498
Subject(s) - background subtraction , computer science , algorithm , segmentation , field (mathematics) , identification (biology) , strengths and weaknesses , subtraction , artificial intelligence , noise (video) , machine learning , data mining , pixel , mathematics , arithmetic , image (mathematics) , philosophy , botany , epistemology , pure mathematics , biology
Background subtraction is the first and basic stage in video analysis and smart surveillance to extract moving objects. In fact, the background subtraction library (BGSLibrary) was created by Andrews Sobral in 2012, which currently combines 43 background subtraction algorithms from the most popular and widely used in the field of video analysis. Each algorithm has its own characteristics, strengths and weaknesses in extracting moving objects. The evaluation allows the identification of these characteristics and helps researchers to design the best methods. Unfortunately, the literature lacks a comprehensive evaluation of the algorithms included in the library. Accordingly, the present work will evaluate these algorithms in the BGSLibrary through the segmentation performance, execution time and processor, so as to, achieve a perfect, comprehensive, real-time evaluation of the system. Indeed, a background modeling challenge (BMC) dataset was selected using the synthetic video with the presence of noise. Results are presented in tables, columns and foreground masks.