Research on the algorithm of infrared target detection based on the frame difference and background subtraction method

2015 ◽  
Author(s):  
Yun Liu ◽  
Yuejin Zhao ◽  
Ming Liu ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Target Detection ◽  
Background Subtraction ◽  
Subtraction Method ◽  
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.

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.

scholarly journals Study on the Automatic Basketball Shooting System Based on the Background Subtraction Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Hu ◽  
Yong Xu
Keyword(s):  
Target Detection ◽  
Background Subtraction ◽  
Detection System ◽  
Moving Target ◽  
Subtraction Method ◽  
Moving Target Detection ◽  
Flow Method ◽  
Optimal Flow ◽  
Optical Flow Method ◽  
Basketball Shooting

There are many drawbacks such as clustering, background updating, inaccurate testing results, and low anti-interference performance in traditional moving target detection theory. In our study, a background subtraction method to automatically capture the basketball shooting trajectory was used to eliminate the drawbacks of the fixed-point shooting system such as cumbersome installation and time and manpower consumption. It also can improve the accuracy and efficiency of moving target detection. We also synthetically compared to common methods including the optical flow method and interframe difference method. Results showed that the background subtraction method has better accuracy with an accuracy rate over about 90% than the interframe subtraction method (88%) and the optimal flow method (85%) and presents excellent robustness with considering variable speed and nonrigid objects. Meanwhile, the automatic detection system for basketball shooting based on background subtraction is built by coupling background subtraction with detection characteristics. The system detection speed built is further accelerated, and the image denoising is improved. The trajectory error rate is about 0.3, 0.4, and 0.5 for the background subtraction method, interframe subtraction method, and optimal flow method, respectively.

A novel background subtraction method based on color invariants

2013 ◽  
Vol 117 (11) ◽  
pp. 1589-1597 ◽  
Author(s):  
Hong Zhou ◽  
Yiru Chen ◽  
Rong Feng
Keyword(s):  
Background Subtraction ◽  
Subtraction Method
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