scholarly journals A Modified Frame Difference Method Using Correlation Coefficient for Background Subtraction

2016 ◽  
Vol 93 ◽  
pp. 478-485 ◽  
Author(s):  
P. Ramya ◽  
R. Rajeswari
Keyword(s):  
Correlation Coefficient ◽  
Difference Method ◽  
Background Subtraction ◽  
Frame Difference

Object Tracking Algorithm Based on Camshift Combining Background Subtraction with Three Frame Difference

2013 ◽  
Vol 373-375 ◽  
pp. 1116-1119 ◽  
Author(s):  
Quan Tang ◽  
Shu Guang Dai ◽  
Jie Yang
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Difference Method ◽  
Background Subtraction ◽  
Tracking Algorithm ◽  
Target Area ◽  
Characteristic Parameters ◽  
Subtraction Method ◽  
Frame Difference ◽  
Camshift Algorithm

Camshift tracking algorithm is based on probability distribution of color , it is susceptible to be interfered by the same color in the background, which will lead to the failure of the target tracking. To overcome this problem it presented an improved Camshift tracking algorithm. It combined background subtraction method with three frame difference method to detect target, got rectangular characteristic parameters of the motion target area as the Camshift initialization parameters, replaced the general Camshift algorithm which is based on color feature. Experimental results show that Camshift algorithm combining the background subtraction method with three frame difference method can meet the requirements of the real-time and stability to a certain extent.

Improved Moving Target Detection Technology

2014 ◽  
Vol 971-973 ◽  
pp. 1628-1632 ◽  
Author(s):  
Xiao Hui Jin ◽  
Wei Yang ◽  
Qian Jin Liu ◽  
Di Zhao ◽  
Sheng Xu
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Detection System ◽  
Ccd Camera ◽  
Target Image ◽  
Target Region ◽  
Difference Image ◽  
Frame Difference ◽  
Detection Technology ◽  
The Difference

In order to detect target clearly, a detection system based on DM642 was designed. The system used improved frame-difference method combined with the background subtraction to detect target. First, the CCD camera scanned the surroundings step by step, then the background model was built, and improved three-frame-difference method was used to get the three-frame-difference image. The target image was the difference of target region extracted by three-frame-difference method and the target region extracted by background subtraction method. Experiments showed that the target image had less interference and a clear profile.

A Motion Image Detection Method Based on the Inter-Frame Difference Method

2014 ◽  
Vol 490-491 ◽  
pp. 1283-1286 ◽  
Author(s):  
Yuan Hang Cheng ◽  
Jing Wang
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Background Subtraction ◽  
Vision System ◽  
Robot Vision ◽  
Main Idea ◽  
Image Method ◽  
Detection Model ◽  
Difference Image ◽  
Frame Difference

Mobile robot vision system based on image information on environment, to make it automatic separation from obstacles and achieve precise mathematical description of obstacles, we construct detection model which combined by the frame difference method and background subtraction for target detection, comprehensive utilization of the main idea of three frame difference image method, the background subtraction and frame difference method combined to complement each other, thereby overcoming each other's weaknesses and improving the effect of target detection, experiment results show that this method can effectively improve the efficiency of target detection.

Fusion of Background Subtraction and Frame Difference in HSV Space for Multi-Object Detection

2013 ◽  
Vol 380-384 ◽  
pp. 3895-3899
Author(s):  
Ya Ne Wen ◽  
Hong Song Li ◽  
Hao Zhou ◽  
Li Ping Tang ◽  
Jun Qi She
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Gaussian Mixture ◽  
Test Results ◽  
Subtraction Method ◽  
False Detection ◽  
Strong Light ◽  
Fusion Frame ◽  
Frame Difference ◽  
Light And Shadow

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

Adaptive Moving Vehicle Detection Algorithm Based on Hybrid Background Subtraction and Frame Difference

2014 ◽  
Vol 644-650 ◽  
pp. 930-933 ◽  
Author(s):  
Yan Li Luo ◽  
Han Lin Wan ◽  
Li Xia Xue ◽  
Qing Bin Gao
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Target Image ◽  
Moving Vehicle ◽  
Frame Difference ◽  
Video Frequency ◽  
Background Difference ◽  
Moving Vehicle Detection ◽  
Initial Target

This paper proposes an adaptive moving vehicle detection algorithm based on hybrid background subtraction and frame difference. The background image of continuous video frequency is reconstructed by calculating the maximun probability grayscale using grey histogram; Moving regions is gained by frame defference, the initial target image is obtained by background difference method,moving regions image and initial target image AND,XOR and OR operations to get the vehicle moving target images. Experimental results show that the algorithm can response timely to the actual scene changes and improve the quality of moving vehicle detection.

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