Comparative Study of Statistical background Modeling and Subtraction

Moussa, Mourad; Hmila, Maha; Douik, Ali

doi:10.11591/ijeecs.v8.i2.pp287-295

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In this paper, various techniques are used for BSOM for foreground object detection and extraction [18][19][20][21]. Following section of the paper discusses techniques and its implementation:…”

Section: Techniques Used For Bsommentioning

confidence: 99%

Foreground algorithms for detection and extraction of an object in multimedia

Rekha¹,

Natarajan²,

Rose³

2020

IJECE

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Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.

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Section: Techniques Used For Bsommentioning

confidence: 99%

Foreground algorithms for detection and extraction of an object in multimedia

Rekha¹,

Natarajan²,

Rose³

2020

IJECE

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“…Background subtraction is one of the commonly used background removals to detect the object in image sequences. However, background subtraction method has limitation in extracting fine human object from an uncontrolled environment due to illumination [11]. This limitation was led to the failure of full human feature extraction for further analysis.…”

Section: Introductionmentioning

confidence: 99%

Stereo Vision Human Motion Detection and Tracking in Uncontrolled Environment

Seng¹,

Lim²,

Gopal³

et al. 2018

TELKOMNIKA

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Stereo vision in detecting human motion is an emerging research for automation, rob otics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hyb rid filter approach is proposed to detect human motion in the stereo vision. The hyb rid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hyb rid approach is compared with the normalized filter. As a result, the proposed approach produces b etter skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, ob tains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hyb rid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.

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“…By combining the color, motion probability and depth cues, the approach has segmented the moving objects. With importance to statistical learning, an efficient method for image segmentation was presented in [19]. The method applies the Rayleigh distribution to compute the probability density of background pixel.…”

Section: Introductionmentioning

confidence: 99%

Strategy for Foreground Movement Identification Adaptive to Background Variations

Anuradha¹,

Raajan²

2018

IJECE

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<p>Video processing has gained a lot of significance because of its applications in various areas of research. This includes monitoring movements in public places for surveillance. Video sequences from various standard datasets such as I2R, CAVIAR and UCSD are often referred for video processing applications and research. Identification of actors as well as the movements in video sequences should be accomplished with the static and dynamic background. The significance of research in video processing lies in identifying the foreground movement of actors and objects in video sequences. Foreground identification can be done with a static or dynamic background. This type of identification becomes complex while detecting the movements in video sequences with a dynamic background. For identification of foreground movement in video sequences with dynamic background, two algorithms are proposed in this article. The algorithms are termed as Frame Difference between Neighboring Frames using Hue, Saturation and Value (FDNF-HSV) and Frame Difference between Neighboring Frames using Greyscale (FDNF-G). With regard to F-measure, recall and precision, the proposed algorithms are evaluated with state-of-art techniques. Results of evaluation show that, the proposed algorithms have shown enhanced performance.</p>

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Comparative Study of Statistical background Modeling and Subtraction

Cited by 5 publications

References 6 publications

Foreground algorithms for detection and extraction of an object in multimedia

Foreground algorithms for detection and extraction of an object in multimedia

Stereo Vision Human Motion Detection and Tracking in Uncontrolled Environment

Strategy for Foreground Movement Identification Adaptive to Background Variations

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