scholarly journals Moving Target Detection Algorithm Research Based on Background Subtraction Method

2013 ◽  
Author(s):  
Wan Songlin ◽  
Liang Haodong ◽  
Wang Taifang ◽  
Cui Zhi
Keyword(s):  
Target Detection ◽  
Background Subtraction ◽  
Detection Algorithm ◽  
Moving Target ◽  
Subtraction Method ◽  
Moving Target Detection

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.

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

scholarly journals Recognition of Student Classroom Behaviors Based on Moving Target Detection

2021 ◽  
Vol 38 (1) ◽  
pp. 215-220
Author(s):  
Bin Wu ◽  
Chunmei Wang ◽  
Wei Huang ◽  
Da Huang ◽  
Hang Peng
Keyword(s):  
Target Detection ◽  
Classroom Environment ◽  
Skin Color ◽  
Student Behavior ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Moving Target ◽  
Classroom Behaviors ◽  
Moving Target Detection

Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

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