scholarly journals MULTIPLE CLASSIFICATION ALGORITHMS UNIMODAL AND MULTIMODAL TARGET RECOGNITION SYSTEMS

2021 ◽  
Vol 28 (3) ◽  
pp. 87-95
Author(s):  
Veaceslav Perju ◽  
Keyword(s):  
Target Recognition ◽  
General Structure ◽  
Threshold Level ◽  
Recognition System ◽  
Classification Algorithms ◽  
Multimodal Systems ◽  
Multiple Classification ◽  
Recognition Probability ◽  
Value Determination ◽  
Correct Target

Target recognition is of great importance in military and civil applications – object detection, security and surveillance, access and border control, etc. In the article the general structure and main components of a target recognition system are presented. The characteristics such as availability, distinctiveness, robustness, and accessibility are described, which influence the reliability of a TRS. The graph presentations and mathematical descriptions of a unimodal and multimodal TRS are given. The mathematical models for a probability of correct target recognition in these systems are presented. To increase the reliability of TRS, a new approach was proposed – to use a set of classification algorithms in the systems. This approach permits the development of new kinds of systems - Multiple Classification Algorithms Unimodal and Multimodal Systems (MAUMS and MAMMS). The graph presentations, mathematical descriptions of the MAUMS and MAMMS are described. The evaluation of the correct target recognition was made for different systems. The conditions of systems' effectiveness were established. The modality of the algorithm's recognition probabilitymaximal value determination for an established threshold level of the system's recognition probability was proposed, which will describe the requirements for the quality and, respectively, the costs of the recognition algorithms. The proposed theory permits the system's design depending on a predetermined recognition probability.

scholarly journals The Effect of Informing Participants of the Response Bias of an Automated Target Recognition System on Trust and Reliance Behavior

2021 ◽  
pp. 001872082110217
Author(s):  
Shala Knocton ◽  
Aren Hunter ◽  
Warren Connors ◽  
Lori Dithurbide ◽  
Heather F. Neyedli
Keyword(s):  
Response Bias ◽  
Target Recognition ◽  
Rate Sensitivity ◽  
High Sensitivity ◽  
Recognition System ◽  
Decision Criterion ◽  
Detection Task ◽  
Mine Detection ◽  
Rate Condition ◽  
Consistent Response

Objective To determine how changing and informing a user of the false alarm (FA) rate of an automated target recognition (ATR) system affects the user’s trust in and reliance on the system and their performance during an underwater mine detection task. Background ATR systems are designed to operate using a high sensitivity and a liberal decision criterion to reduce the risk of the ATR system missing a target. A high number of FAs in general may lead to a decrease in operator trust and reliance. Methods Participants viewed sonar images and were asked to identify mines in the images. They performed the task without ATR and with ATR at a lower and higher FA rate. The participants were split into two groups—one informed and one uninformed of the changed FA rate. Trust and/or confidence in detecting mines was measured after each block. Results When not informed of the FA rate, the FA rate had a significant effect on the participants’ response bias. Participants had greater trust in the system and a more consistent response bias when informed of the FA rate. Sensitivity and confidence were not influenced by disclosure of the FA rate but were significantly worse for the high FA rate condition compared with performance without the ATR. Conclusion and application Informing a user of the FA rate of automation may positively influence the level of trust in and reliance on the aid.

scholarly journals Hierarchical Recognition System for Target Recognition from Sparse Representations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zongyong Cui ◽  
Zongjie Cao ◽  
Jianyu Yang ◽  
Hongliang Ren
Keyword(s):  
Deep Learning ◽  
Deep Structure ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Feature Representation ◽  
Small Data ◽  
Learning Method ◽  
Classification Problems ◽  
Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

Target recognition system of dynamic scene based on artificial intelligence vision

2018 ◽  
Vol 35 (4) ◽  
pp. 4373-4383 ◽  
Author(s):  
Jianzhong Yang ◽  
Xianyang Li ◽  
Yu Jiang ◽  
Guihua Qiu ◽  
S. Buckdahn
Keyword(s):  
Artificial Intelligence ◽  
Target Recognition ◽  
Recognition System ◽  
Dynamic Scene

scholarly journals A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2321 ◽  
Author(s):  
Myoungseok Yu ◽  
Narae Kim ◽  
Yunho Jung ◽  
Seongjoo Lee
Keyword(s):  
Real Time ◽  
Conventional Method ◽  
Gesture Recognition ◽  
Detection Method ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Probability ◽  
Recognition Systems ◽  
Cw Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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