The Improvement of the Background Subtraction and Shadow Detection in Grayscale Video Sequences

2007 ◽  
Author(s):  
Yung-Gi Wu ◽  
Chung-Ying Tsai
Keyword(s):  
Background Subtraction ◽  
Shadow Detection ◽  
Video Sequences

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 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.

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