A block-based background model for moving object detection

Elharrouss, Omar; Abbad, Abdelghafour; Moujahid, Driss; Riffi, Jamal; Tairi, Hamid

doi:10.5565/rev/elcvia.855

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…The evaluation of the deep learning model performance computed in the testing phase was based on the segmentation metrics [ 34 , 35 ]. These metrics are defined as follows: Precision : This calculates how close the values are to each other and how close they are to the true values.…”

Section: Experimental Resultsmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

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Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

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Section: Experimental Resultsmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

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“…In [24], authors registered the background pixels by collecting the blocks of background [4], utilized the circular shift method on the neighbourhood of each pixel. It evaluated the moving object by utilizing the background subtraction and graph cut techniques.…”

Section: Related Workmentioning

confidence: 99%

Moving Object Localization in Video Sequences under Static and Dynamic Background Conditions

Kumar¹,

Yadav²,

Manoj³

et al. 2019

IJIES

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Background subtraction is one of the most reliable approach to localize the moving object under static camera arrangement. As seen, the moving object detection is a preliminary task in many vision applications such as video analysis, object tracking and activity analysis. However, the quasi-stationary pixels, aperture effect, ghost trail and varying illumination are still an annoying factors in the extraction procedures of the actual moving object in video. To alleviate the above problems, a video segmentation method is proposed that utilizes background subtraction and the 3-class fuzzy c-means clustering algorithm for extracting the relevant moving pixels. The proposed algorithm modifies learning parameters of adaptive filters to adapt the changes in the background. Afterwards, it incorporates the Markovian framework in which the initial motion field provides a prior information to regularize the segmentation process. The method achieves better visual and quantitative performance than other well-known background subtraction methods reported in this paper.

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“…Bir diğer dezavantajı ise metodun ışık şiddeti değişimlerinden etkilenmesidir. Her iki etkinin de azaltılması için arka fonu adaptif olarak ayarlan çalışmalar yapılmaktadır [6], [7].…”

Section: Introductionunclassified

A Novel Approach for Automatic Detection and Tracking of Flying Objects

Pakfiliz¹

2019

Pamukkale J Eng Sci

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Bu çalışmada yerden havaya takip görevlerinde kullanılan video sistemlerinin uçan nesneleri otomatik olarak tespit ve takip etmesi için yeni bir metot sunulmaktadır. Bu yaklaşımda uçan bir nesnenin varlığının tespiti için Standart Sapma bilgisinin kullanıldığı bir metot geliştirilmiştir. Tespit sonrası takip için ölçüm verisi takibe uygun hale getirilir, bu amaçla uçan nesnenin arka fona göre daha baskın hale gelmesi sağlanır. Hedefin takibi için gerçek zamanlı performans verebilen genlik bilgisi ilave edilmiş Etkileşimli Çoklu Model Olasılıksal Veri İlişkilendirme (EÇMOVİ-GB) algoritması kullanılmıştır. EÇMOVİ-GB algoritması temelde nokta verisi takibinde kullanılan bir algoritma olmakla birlikte bu çalışmada video takibinde kullanılabilirliği gösterilmiştir. Bu amaçla örneklenen video çerçevelerinin genlik bilgileri uygun olarak kodlanarak nokta verisi haline getirilir ve takip bu veri üzerinden gerçekleştirilir. Böylece hedefin otomatik olarak tespit edildiği, takibin başlatıldığı ve sürdürüldüğü bir algoritma geliştirilmiştir. Algoritma değişik manevra, hedef tipleri ve arka fon gürültü durumları için incelenerek, başarılı sonuçlar elde edilmiştir. In this study, a new method is presented to automatically detect and track flying objects through video systems that are used for surface to air tracking tasks. In this approach, a method has been developed in which Standard Deviation is used to determine the presence of a flying object. The measurement data is adapted to track, so that the flying object becomes more dominant than the background. In order to track the detected target in real time, Interacting Multiple Model Probabilistic Data Association with Amplitude Information (IMMPDA-AI) algorithm is used. Although the IMMPDA-AI algorithm is mainly a point tracking algorithm, in this study, its applicability to video tracking is shown. For this purpose, the amplitude information of the sampled video frames is encoded as point data and the tracking is performed on this data. Thus, an algorithm has been developed in which the target is automatically detected, track initiated and continued. The algorithm is evaluated for different maneuvers, target types and clutter situations, and successful results are obtained.

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A block-based background model for moving object detection

Cited by 11 publications

References 27 publications

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Moving Object Localization in Video Sequences under Static and Dynamic Background Conditions

A Novel Approach for Automatic Detection and Tracking of Flying Objects

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