scholarly journals Compressive Background Modeling for Foreground Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Qian Lu ◽  
Dianhong Wang ◽  
Wei Liu
Keyword(s):  
Background Subtraction ◽  
Background Modeling ◽  
Video Sequences ◽  
Computationally Efficient ◽  
Subtraction Method ◽  
Foreground Extraction ◽  
Image Frame ◽  
Computer Vision Applications ◽  
Background Image ◽  
Classical Background

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.

The Design and Implementation of Moving Object Detecting Algorithm in Intelligent Video Surveillance

2015 ◽  
Vol 738-739 ◽  
pp. 779-783
Author(s):  
Jin Hua Sun ◽  
Cui Hua Tian
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Image Sequence ◽  
Background Modeling ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Design And Implementation

In view of the problems existed in moving object detection in video surveillance system, background subtraction method is adopted and combined with Surendra algorithm for background modeling, an algorithm of detecting moving object from video is proposed, and OpenCV programming is adopted in Visual c ++ 6.0 for implementation. Experimental results indicate that the algorithm can accurately detect and identify moving object in video by reading the image sequence of surveillance video, the validity of the algorithm is verified.

Improved Background Subtraction Technique for detecting Moving Object

2020 ◽  
Vol 13 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Image Enhancement ◽  
Background Subtraction ◽  
Moving Object ◽  
Detection Methods ◽  
Subtraction Method ◽  
Subtraction Technique ◽  
Visual Background ◽  
Computer Vision Applications

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

Real-Time Human Skeleton Extraction Based on Video Sequences

2013 ◽  
Vol 401-403 ◽  
pp. 1410-1414
Author(s):  
Qing Ye ◽  
Jun Feng Dong ◽  
Yong Mei Zhang
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Adaptive Threshold ◽  
Video Sequences ◽  
Topological Properties ◽  
Skeleton Extraction ◽  
Subtraction Method ◽  
Computation Cost ◽  
Thinning Algorithm ◽  
Moving Body

Thinning algorithm is widely used in image processing and pattern recognition.In this paper we proposed an optimized thinning algorithm based on Zhan-Suen thinning and applied it to video sequences of moving human body to extract real-time body skeleton. We firstly used background subtraction method to detect moving body, then made use of adaptive threshold segmentation to gain the binary moving body image, finally we used the optimized algorithm to the binary image and got its skeleton. The skeleton not only maintains the movement geometry and body image’s topological properties, also reduces image redundancy and computation cost, and helps us clearly recognize the moving body posture.

scholarly journals Efficient FPGA Implementation of Human Detection from Video Sequences

2020 ◽  
Vol 9 (6) ◽  
pp. 242-248
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Median Filter ◽  
Region Of Interest ◽  
Video Stream ◽  
Human Detection ◽  
Video Sequences ◽  
Computer Vision Applications ◽  
And Performance ◽  
Better Than

Detection of Human is a vital and difficult task in computer vision applications like a police investigation, vehicle tracking, and human following. Human detection in video stream is very important in public security management. In such security related cases detecting an object in the video, sequences are very important to understand the behavior of moving objects which normally used in the background subtraction technique. The input data is preprocessed using a modified median filter and Haar transform. The region of interest is extracted using a background subtraction algorithm with remaining spikes removed using threshold technique. The proposed architecture is coded using standard VHDL language and performance is checked in the Spartan-6 FPGA board. The comparison result shows that the proposed architecture is better than the existing method in both hardware and image quality

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.

Research of Background Modeling Algorithm Method Based on Multi-Frame Average Method in Moving Target Detection

2013 ◽  
Vol 380-384 ◽  
pp. 1390-1393
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu
Keyword(s):  
Background Subtraction ◽  
Surveillance System ◽  
Average Method ◽  
Adaptive Threshold ◽  
Background Modeling ◽  
Moving Target ◽  
Video Surveillance System ◽  
Intelligent Video Surveillance System ◽  
The Common ◽  
Background Image

In intelligent video surveillance system, there exists the defect that the background is multimodal when extracting the background image, so the extracted foreground objects are not very pure with the method of background subtraction. In this paper, we put forward an algorithm which adopts adaptive threshold to update the background. Firstly, the common background model algorithms and their defect are analysed. Then we propose the strategy of updating background template based on adaptive threshold and then give the specific algorithm steps. Finally, the algorithm is simulated and the results are analyzed.

A Background Subtraction Method for Defect Detection of Printed Image

2013 ◽  
Vol 462-463 ◽  
pp. 421-427
Author(s):  
Jian Hua Ding ◽  
Yao Lu ◽  
Wei Huang ◽  
Ming Qin
Keyword(s):  
Defect Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Texture Feature ◽  
Background Modeling ◽  
Experimental Results ◽  
Subtraction Method ◽  
Moving Objects Detection ◽  
Hsv Histogram ◽  
Objects Detection

Background subtraction is often used to detect the moving objects from static cameras. The difficult of defect detecting of printing matter is how to detect the unknown flaws in complicate background effectively. Inspired by the background modeling approach for moving objects detection, a background modeling method in defect detection of printed image is suggested in this paper. Those pixels without defects are regarded as background, while the flaw pixels are defined as foreground. Firstly, we select LBP histogram as texture feature and HSV histogram as color feature to model the background respectively. Then, lots of printed images in which there are no defects are used to update these two models. Finally, we utilize the models to detect defects of printing images. Experimental results show that this background model works well in our defect detection.

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