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.

A Moving Objects Detection Method Base on Improved GMM

2011 ◽  
Vol 225-226 ◽  
pp. 637-641
Author(s):  
Yi Bing Li ◽  
He Jiang Jia ◽  
Ao Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
False Detection ◽  
Moving Objects Detection ◽  
Method Base ◽  
Matching Mechanism ◽  
Computer Vision Applications ◽  
Objects Detection

Motion detection is the first and important step in many computer vision applications. Gaussian mixture model is an effective way for moving objects detection, but there are some shortcomings of this model such as slow updating rate and false detection in complex background. In this paper, we proposed an improved Gaussian mixture model method. A matching distance is defined to compute the learning rate when updating the models, and we also use dual threshold to improve the matching mechanism. Experimental results show that this method can get a faster adaptation to background and better contour of the moving objects.

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 Method of Moving Region Detection for Static Camera

2018 ◽  
Vol 12 (6) ◽  
pp. 3626-3633
Author(s):  
Pravesh Kumar Goel ◽  
Paresh P. Kotak ◽  
Amit Gupta
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Region Detection ◽  
Computer Vision Applications ◽  
Object Center ◽  
Background Image

The moving object detection from a stationary video sequence is a primary task in various computer vision applications. In this proposed system; three processing levels are suppose to perform: detects moving objects region from the background image; reduce noise from the pixels of detected region and extract meaningful objects and their features (area of object, center point of area etc.). In this paper; background subtraction techniques is used for segments moving objects from the background image, which is capable for pixel level processing. Morphology operation (Erosion and dilation) are used to remove pixel to pixel noise. In last level, CCL algorithm is used for sorts out foregrounds pixels are grouped into meaningful connected regions and their features.

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
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