Background subtraction using Artificial Immune Recognition System and Single Gaussian (AIRS-SG)
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Background subtraction using Artificial Immune Recognition System and Single Gaussian (AIRS-SG) Wafa Nebili1 · Brahim Farou1 · Hamid Seridi1 Received: 17 May 2019 / Revised: 4 April 2020 / Accepted: 13 April 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Background subtraction is an essential step in the video monitoring process. Several models have been proposed to differentiate background pixels from foreground pixels. However, most of these methods fail to distinguish them in highly dynamic environments. In this paper, we propose a new method robust and more efficient for distinguishing moving objects from static objects in dynamic scenes. For this purpose, we propose to use a bio-inspired approach based on the Artificial Immune Recognition System (AIRS) as a classification tool. AIRS separates antibodies, represented by the pixels of the background model, from the antigens that model foreground pixels representing moving objects. Each pixel is modeled by a feature vector containing the attributes of a Gaussian. Only the pixels classified as background are taken into account by the system and updated in the model. This combination has allowed to benefit from two advantages: the power of AIRS to provide an online update of system parameters and the ability of Gaussians to adapt to scene variations at the pixel level. To test the proposed approach, six videos representing the dynamic background category of the CDnet 2014 dataset are selected. Obtained results proved the effectiveness of this new process in terms of quality and complexity compared to other state-of-the-art methods. Keywords Video surveillance · SG · Background subtraction · Moving objects · Foreground pixel · AIRS
1 Introduction Background subtraction (BS) also called motion detection or foreground detection, is a crucial step in many computer vision application like: video surveillance [7, 35], multimedia Wafa Nebili
[email protected] Brahim Farou [email protected] Hamid Seridi [email protected] 1
LabSTIC, 8 mai 1945 Guelma University, POB 401, 24000 Guelma, Algeria
Multimedia Tools and Applications
[11] and optical motion capture [5], etc. BS consists to model the background before detecting the moving objects (foreground). Generally, the moving objects are a humans, cars, texts, etc. The intuitive way to model, the background is to train the system using a set of frames devoid of moving objects. After that, we applied an on-line or off-line process to extract the foreground from frames. In the on-line process, the background model updated during the whole execution to pick up any new changes in the background within the video sequence, but in the off-line process, the background model is unchanged. An efficient method for detection moving objects must ensure a good separation between the background and the foreground, with gain in execution time and memory space. Single Gaussian (SG) is among the most popular methods that have achieved great success in the detection of mo
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