Comparison of SVM and CNN classification methods for infrared target recognition

2018 ◽  
Author(s):  
Ozan Yardimci ◽  
Barış Çağlar Ayyıldız
Keyword(s):  
Target Recognition ◽  
Classification Methods

Long range target recognition and identification of camouflaged armored vehicles.

1979 ◽  
Author(s):  
William L. Warnick ◽  
Garvin D. Chastain ◽  
William H. Ton
Keyword(s):  
Long Range ◽  
Target Recognition ◽  
Armored Vehicles

Target recognition on complex displays.

1959 ◽  
Author(s):  
Charles A. Baker ◽  
Dominic F. Morris ◽  
William C. Steedman
Keyword(s):  
Target Recognition

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Sign in / Sign up

Export Citation Format

Share Document