Background subtraction based on a Self-Adjusting MoG

Wafa NEBİLİ; Samir HALLACİ; Brahim FAROU

Makale detayları

Background subtraction based on a Self-Adjusting MoG

Yazarlar:

Wafa NEBİLİ | Samir HALLACİ | Brahim FAROU

Yayıncı:

International Society of Academicians

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

International Journal of Informatics and Applied Mathematics 2019 Vol.2 Issue 1, pp.73-84

Belge Tipi:

Makale

Yayın Tarihi:

9/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000719269

Sıklık:

İki Yıllık

eISSN:

2667-6990

Özet:

 


The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems. In this paper we present a new method that allows better detection of moving objects. This method combine the robustness of the Artificial Immune Recognition System (AIRS) with respect to the local variations and the power of Gaussian mixtures (MoG) to model changes at the pixel level. The task of the AIRS is to generate several MoG models for each pixel. This models are filtred through two mecanism: the competition for resources and the development of a candidate memory cell. The best model is merged with the exesting MoG according to the Memory cell introduction process. Obtained results on the Wallflower dataset proved the performance of our system compared to other state-of-the-art methods.

.

 


The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems.

In this paper we present a new method that allows better detection of moving objects. This method combine the robustness of the Artificial Immune Recognition System (AIRS) with respect to the local variations and the power of Gaussian mixtures (GMM) to model changes at the pixel level.

The task of the AIRS is to generate several GMM models for each pixel. This models are filtred through two mecanism: the competition for resources and the development of a candidate memory cell. The best model is merged with the exesting GMM according to the Memory cell introduction process.

results obtained on the Wallflower dataset proved the performance of our system compared to other state-of-the-art methods.

.

Makale detayları

Background subtraction based on a Self-Adjusting MoG

Yazarlar: Wafa NEBİLİ | Samir HALLACİ | Brahim FAROU

Yayıncı: International Society of Academicians

Ana Başlıklar: Veri işleme & bilgisayar bilimi

Dergi: International Journal of Informatics and Applied Mathematics 2019 Vol.2 Issue 1, pp.73-84

Belge Tipi: Makale

Yayın Tarihi: 9/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000719269

Sıklık: İki Yıllık

eISSN: 2667-6990

Sayı

Yazarlar:

Wafa NEBİLİ | Samir HALLACİ | Brahim FAROU

Yayıncı:

International Society of Academicians

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

International Journal of Informatics and Applied Mathematics 2019 Vol.2 Issue 1, pp.73-84

Belge Tipi:

Makale

Yayın Tarihi:

9/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000719269

Sıklık:

İki Yıllık

eISSN:

2667-6990