Efficient parallelization of GMM background subtraction algorithm on a multi-core platform for moving objects detection

2018 ◽  
Author(s):  
Lhoussein Mabrouk ◽  
Sylvain Huet ◽  
Dominique Houzet ◽  
Said Belkouch ◽  
Abdelkrim Hamzaoui ◽  
...  
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Efficient Parallelization

scholarly journals Background Subtraction (BS) Using Instant Pixel Histogram

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Narjis Mezaal Shati ◽  
Sundos Abdulameer Alazawi ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Computer Vision ◽  
False Alarm ◽  
False Alarm Rate ◽  
Background Subtraction ◽  
Detection Rate ◽  
Moving Objects ◽  
Moving Objects Detection ◽  
Computer Vision Applications ◽  
Objects Detection ◽  
Published Research

Video computer vision applications require moving objects detection as a first phase of their operation. Therefore, background subtraction (BS), an investigate branch in computer vision with intensive published research, is applied to obtain the “background” and the “foreground.” Our study proposes a new BS model that utilizes instant pixel histogram, which is implemented to extract foreground objects from two datasets, the first Visor (different human actions) and the second Anomaly Detection Dataset UCSD (Peds2). The model when using the Visor dataset gives 100% detection rate with 8% false alarm rate, whereas, when using UCSD (Peds2), it achieves a detection rate and false alarm rate of 77% and 34% respectively.

Study of Moving Objects Detection Method for Examination Room Surveillance Based on Background Subtraction

2013 ◽  
Vol 718-720 ◽  
pp. 385-388
Author(s):  
Yong Zheng Lin ◽  
Pei Hua Liu
Keyword(s):  
Background Subtraction ◽  
Surveillance System ◽  
Moving Objects ◽  
Detection Method ◽  
Detection Algorithm ◽  
System Stability ◽  
Moving Objects Detection ◽  
System A ◽  
Objects Detection ◽  
Examination Room

Detection of moving objects is one of the primary factors to influence the examination surveillance system. A new moving objects detection algorithm based on background subtraction is presented after the introduction various of existing methods. Dynamic threshold conception is put forward while defining threshold. Practices show that this method can successfully overcome lighting variations and the system stability is improved.

Moving Object Detection and Tracking in outside Environments

2011 ◽  
Vol 130-134 ◽  
pp. 3862-3865
Author(s):  
Yi Ding Wang ◽  
Da Qian Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Middle Ground ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection ◽  
Typical Method

Background subtraction is a typical method for moving objects detection. The Gaussian mixture model is one of widely used method to model the background. However, in challenge environments, quick lighting changes, noises and shake of background can influence the detection of moving objects significantly. To solve this problem, an improved Gaussian Mixture Model is proposed in this paper. In the proposed algorithm, Objects are divided into three categories, foreground, background and middle-ground. The proposed algorithm is a segmented process. Moving objects including foreground and middle-ground are extracted firstly; then foreground is segmented from middle-ground. In this way almost middle-ground are filtered, so we can obtain a clear foreground objects. Experimental results show that the proposed algorithm can detect moving objects much more precisely, and it is robust to lighting changes and shadows.

scholarly journals Dynamic ARMA-Based Background Subtraction for Moving Objects Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128659-128668
Author(s):  
Jian Li ◽  
Zhong-Ming Pan ◽  
Zhuo-Hang Zhang ◽  
Heng Zhang
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Moving Objects Detection ◽  
Objects Detection

scholarly journals CORRECTING FALSE SEGMENTATION IN VIDEO USING IMAGE OVER-SEGMENTATION

2013 ◽  
pp. 174-178
Author(s):  
SHAILAJA SURKUTLAWAR SURKUTLAWAR ◽  
RAMESH K KULKARNI
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Colour Space ◽  
Good Balance ◽  
Moving Objects Detection ◽  
Mixture Of Gaussians ◽  
Hsv Colour Space ◽  
Objects Detection ◽  
Shadow Suppression ◽  
Confidence Measurement

Moving objects detection is a fundamental step in many vision based applications. Background subtraction is the typical method. When scene exhibits pertinent dynamism method based on mixture of Gaussians is a good balance between accuracy and complexity, but fails due to two kinds of false segmentations i.e moving shadows incorrectly detected as objects and some actual moving objects not detected as moving objects. In computer vision, segmentation refers to process of partitioning a digital image in to multiple segments and goal of segmentation is to simplify and/or change representation of image in to something that is more meaningful and easier to analyse. A colour clustering based on k-means and image over-segmentation are used to segment the input frame into patches and shadow suppression done by HSV colour space, the outputs of mixture of Gaussians are combined with the colour clustered regions to a module for area confidence measurement. In this way, two major segment errors can be corrected. Experimental results show that the proposed approach can significantly enhance segmentation results.

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