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.

Analysis and Improvement in Color-Based Target Recognition Algorithm of Robot

2013 ◽  
Vol 655-657 ◽  
pp. 1043-1047 ◽  
Author(s):  
Dong Bo Zhou ◽  
Ji Cai Deng ◽  
Geng Hui Wang ◽  
Qi Xin Deng
Keyword(s):  
Control System ◽  
Real Time ◽  
Humanoid Robot ◽  
Target Recognition ◽  
Robot Vision ◽  
Recognition Algorithm ◽  
Color Information ◽  
Real Time Control ◽  
Table Lookup ◽  
Time Control

In the Martial Arts arena contest of robot, Humanoid robot should recognize the target timely and accurately. So robot vision technology becomes a key of the contest. In this paper, target recognition algorithms based on color information are analyzed. According to the results, an improved algorithm based on Table Lookup method is proposed, which aimed to provide more rapidity of computing in real-time control system on the robot. It is shown in illustrative experiment that average 50% time was saved in computing when using the new algorithm instead of traditional algorithms.

Research on Human Movement Target Recognition Algorithm in Complex Traffic Environment

2019 ◽  
Vol 34 (05) ◽  
pp. 2050012 ◽  
Author(s):  
Ying Zou ◽  
Dahu Wang ◽  
Leian Liu
Keyword(s):  
Target Recognition ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Moving Target ◽  
Adaboost Algorithm ◽  
Video Images ◽  
Adaptive Weight ◽  
Traffic Environment

With the increase in the total population of the society and the continuous increase in the number of trips, the traffic pressures faced by people are increasing. With the development and advancement of computer technology, the emergence of intelligent transportation provides a better way to solve the problem of effectively alleviating traffic pressure and reducing the incidence of traffic accidents. In recent years, intelligent traffic monitoring system, as one of the important branches in the field of intelligent transportation, has also received more and more attention. Among them, video-based moving target recognition technology involves theoretical knowledge in various fields such as artificial intelligence, image processing, pattern recognition and computer vision. It is an important means to realize “safe city” and “smart city” and a key technology for intelligent monitoring. Therefore, the research on human motion target recognition algorithm in complex traffic environment has important theoretical and practical value. In the field of intelligent traffic monitoring, the moving target detection and recognition effect of video images will have certain influence on the classification and behavior understanding of subsequent moving targets. In this paper, the commonly used moving target detection methods are studied first, and the convergence problem of the traditional Adaboost algorithm is improved. An Adaboost algorithm based on adaptive weight update is proposed, and then the support vector machine (SVM) is used. The algorithm identifies the detected moving target. Finally, through simulation experiments on the acquired video images, the results show that the proposed human motion target recognition algorithm based on adaptive weight update Adaboost and SVM has good feasibility and rationality.

Air target recognition algorithm based on mixed depth features in the interference environment

Optik ◽  
2021 ◽  
pp. 167535
Author(s):  
Kai ZHANG ◽  
Jiayi WEI ◽  
Tiantian WANG ◽  
LI Shaoyi ◽  
Xi YANG
Keyword(s):  
Target Recognition ◽  
Recognition Algorithm

scholarly journals Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongqiao Wang ◽  
Yanning Cai ◽  
Guangyuan Fu ◽  
Shicheng Wang
Keyword(s):  
Target Recognition ◽  
Low Frequency ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Scale Transformation ◽  
Sar Image ◽  
Sar Images ◽  
Stationary Target ◽  
Multilevel Sampling ◽  
Process Method

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

scholarly journals Kernel Two-Dimensional Nonnegative Matrix Factorization: A New Method to Target Detection for UUV Vision System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jian Xu ◽  
Pengfei Bi ◽  
Xue Du ◽  
Juan Li ◽  
Tianhao Jiang
Keyword(s):  
Target Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Target Recognition ◽  
Vision System ◽  
Dimensional Space ◽  
Nonnegative Matrix ◽  
Two Dimensional ◽  
Basis Matrix ◽  
Underwater Target

This paper studies an advanced intelligent recognition method of underwater target based on unmanned underwater vehicle (UUV) vision system. This method is called kernel two-dimensional nonnegative matrix factorization (K2DNMF) which can further improve underwater operation capability of the UUV vision system. Our contributions can be summarized as follows: (1) K2DNMF intends to use the kernel method for the matrix factorization both on the column and row directions of the two-dimensional image data in order to transform the original low-dimensional space with nonlinearity into a higher dimensional space with linearity; (2) In the K2DNMF method, a good subspace approximation to the original data can be obtained by the orthogonal constraint on column basis matrix and row basis matrix; (3) The column basis matrix and row basis matrix can extract the feature information of underwater target images, and an effective classifier is designed to perform underwater target recognition; (4) A series of related experiments were performed on three sets of test samples collected by the UUV vision system, the experimental results demonstrate that K2DNMF has higher overall target detection accuracy than the traditional underwater target recognition methods.

scholarly journals Badminton motion capture with visual image detection of picking robotics

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096907
Author(s):  
Changxin Li
Keyword(s):  
Image Processing ◽  
Control System ◽  
Motion Capture ◽  
High Precision ◽  
Target Recognition ◽  
Vision System ◽  
Recognition Rate ◽  
Motion Capture System ◽  
Vision Control ◽  
Edge Features

In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

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