scholarly journals Mobile Image Multi-label Recognition Algorithm Based on PaddlePaddle Platform

2021 ◽  
Vol 2066 (1) ◽  
pp. 012046
Author(s):  
Yuanyi Chen
Keyword(s):  
Recognition System ◽  
Reading Rate ◽  
Recognition Algorithm ◽  
Space Diversity ◽  
Frequency Diversity ◽  
Key Factors ◽  
Existing Problems ◽  
Learning Framework ◽  
Intelligent Robots ◽  
Moving Images

Abstract As one of the core algorithms of machine vision, the mobile image multi-label recognition algorithm has received extensive attention from researchers in recent years and has been widely used in cutting-edge fields such as deep learning framework paddlepaddle platform, video surveillance, intelligent robots, and unmanned aerial vehicles. However, the existing recognition algorithms are not completely satisfied with the practical application in life and production. Due to the complexity of the platform environment, they can often only propose specific solutions based on existing problems, and there is no universal algorithm that is suitable for all kinds of Complex environment. The purpose of this paper is to study the multi-label recognition algorithm of moving images based on PaddlePaddle platform. This research mainly analyzes and researches the mobile image multi-tag space deployment plan and the multi-tag recognition algorithm, and further improves the tag reading rate and recognition reliability of the mobile image on the PaddlePaddle platform. This research first analyzes several key factors that affect the performance of UHF recognition system, considers the improvement plan of PaddlePaddle platform’s mobile image multi-tag recognition algorithm from the two aspects of space diversity and frequency diversity, and finally determines the multiple The label space diversity scheme, and the introduction of a multi-label optimization recognition algorithm to improve the recognition efficiency of the PaddlePaddle platform’s mobile image multi-label. Experimental data shows that the reading rate can reach 0.907 when identifying 300 tags in the experiment, and when the number of tags is greater than 300, the reading rate is close to 1, which verifies that the algorithm proposed in this paper is used in the multi-tag recognition of moving images on the PaddlePaddle platform.

scholarly journals Augmented Latent Features of Deep Neural Network-Based Automatic Speech Recognition for Motor-Driven Robots

2020 ◽  
Vol 10 (13) ◽  
pp. 4602
Author(s):  
Moa Lee ◽  
Joon-Hyuk Chang
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Internal Layers ◽  
Acoustic Features ◽  
Word Error Rate ◽  
State Information ◽  
Intelligent Robots ◽  
State Dependent ◽  
Latent Features

Speech recognition for intelligent robots seems to suffer from performance degradation due to ego-noise. The ego-noise is caused by the motors, fans, and mechanical parts inside the intelligent robots especially when the robot moves or shakes its body. To overcome the problems caused by the ego-noise, we propose a robust speech recognition algorithm that uses motor-state information of the robot as an auxiliary feature. For this, we use two deep neural networks (DNN) in this paper. Firstly, we design the latent features using a bottleneck layer, one of the internal layers having a smaller number of hidden units relative to the other layers, to represent whether the motor is operating or not. The latent features maximizing the representation of the motor-state information are generated by taking the motor data and acoustic features as the input of the first DNN. Secondly, once the motor-state dependent latent features are designed at the first DNN, the second DNN, accounting for acoustic modeling, receives the latent features as the input along with the acoustic features. We evaluated the proposed system on LibriSpeech database. The proposed network enables efficient compression of the acoustic and motor-state information, and the resulting word error rate (WER) are superior to that of a conventional speech recognition system.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

Sensor positioning for a human activity recognition system using a double layer classifier

2021 ◽  
pp. 095441192110405
Author(s):  
Mohamed H Abdelhafiz ◽  
Mohammed I Awad ◽  
Ahmed Sadek ◽  
Farid Tolbah
Keyword(s):  
Activity Recognition ◽  
Double Layer ◽  
Prediction Accuracy ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Gait ◽  
System A ◽  
Election Method ◽  
Single Sensor ◽  
Feature Selector

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor’s optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Research and Realization on the Voice Command Recognition System for Robot Control Based on ARM9

2010 ◽  
Vol 44-47 ◽  
pp. 1422-1426
Author(s):  
Mei Juan Gao ◽  
Zhi Xin Yang
Keyword(s):  
Control System ◽  
Speech Recognition ◽  
Mobile Robot ◽  
Robot Control ◽  
Linear Prediction ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Speech Recognition System ◽  
Mobile Robot Control ◽  
Robot Control System

In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as the characteristic parameters. Both dynamic time warping (DTW) and hidden markov model (HMM) are commonly used algorithm. For the different characteristics of DTW and HMM recognition algorithm, two different programs were designed for mobile robot control system. The effect and speed of the two speech recognition system were analyzed and compared.

Design and Algorithm Optimization of P2P Mobile Monitoring Network-Based Facial Recognition System

2014 ◽  
Vol 721 ◽  
pp. 771-774
Author(s):  
Xing Li ◽  
Pin Wang ◽  
Hong Li
Keyword(s):  
Large Scale ◽  
Facial Recognition ◽  
Monitoring Network ◽  
Mobile Terminal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Algorithm Optimization ◽  
Mobile Monitoring ◽  
Network Infrastructure ◽  
Emergency Situations

To meet the demand of provisional emergency monitoring in large-scale outdoor rallies and emergency situations, this paper designs a P2P mobile monitoring system which would be based P2P wireless network infrastructure. The mobile terminal function of the system is designed to be able to make monitoring recording, play videos and realize P2P video session in the form of streaming media. The embedded design and algorithm optimization of the system’s facial recognition function gives more well-rounded performance to the system in terms of safeguarding, dispatch and commanding. This paper would design the system’s P2P network infrastructure and the function module of the mobile terminal software and propose the optimization of the facial recognition algorithm based on this system.

A Real Time Hand Gesture Recognition System Based on DFT and SVM

2013 ◽  
Vol 284-287 ◽  
pp. 3004-3009 ◽  
Author(s):  
Wen Her Chen ◽  
Ching Tang Hsieh ◽  
Tsun Te Liu
Keyword(s):  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Extraction Process ◽  
Recognition Algorithm ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Fast Detection ◽  
Starting Point ◽  
Boundary Deformation

Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Cam-shift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transformation. Finally, outline feature for the nonlinear non-separable type of data was classified by using SVM. Experimental results showed the accuracy is 93.4% in average and demonstrated the feasibility of proposed system.

scholarly journals Identical Twins Facial Recognition System Using Cloud

2020 ◽  
Vol 9 (06) ◽  
pp. 25070-25074
Author(s):  
Chandrakala G Raju ◽  
Rahul S Hangal ◽  
Shashidhara A R ◽  
Srinatha T D
Keyword(s):  
Facial Recognition ◽  
Extreme Case ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Image Features ◽  
Identical Twins ◽  
Training Algorithms ◽  
Data Set ◽  
Current Image ◽  
Facial Recognition System

Facial recognition algorithm should be able to work even when the similar looking people are found i.e. also in the extreme case of identical looking twins. An experimental data set which contains 40 images of 20 pairs of twins collected randomly from the internet. The training is done with the selected images of the twins using different training algorithms and inbuilt functions available. The extracted features are stored over the Amazon public cloud. As a part of testing phase random images from the dataset trained are selected and upon running it over the system we get the features of those images which then will be compared by extracting the features already stored in Amazon cloud. The stored values and the current image features are compared and result will be displayed on the GUI. Identical twin’s facial recognition system uses the machine learning, image processing algorithms and deep learning algorithms. Regardless of the conditions of the images acquired, distinguishing identical twins is significantly harder than distinguishing faces that are not identical twins for all the algorithms.

scholarly journals Automated Bird Species Recognition System Based on Image Processing and Svm Classifier

2021 ◽  
Vol 12 (2) ◽  
pp. 351-356
Author(s):  
Chandra. B, Et. al.
Keyword(s):  
Image Processing ◽  
Species Recognition ◽  
Bird Species ◽  
Image Data ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Data Set ◽  
Learning Techniques ◽  
Processing Techniques

Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.

Automated Technology Integrations for Customer Satisfaction Assessment

2015 ◽  
pp. 606-620 ◽  
Author(s):  
Chien Shing Ooi ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Content Analysis ◽  
Customer Satisfaction ◽  
Main Part ◽  
Recognition System ◽  
Analysis Tool ◽  
Communication Tools ◽  
Existing Problems ◽  
Automated Technology ◽  
The Common ◽  
Text Content

This chapter presents the automated technology integrations for organizations to assess their customer satisfaction. The technology utilizations of most of the organizations to communicate with customers are summarized. This chapter also compares the common resources that are used to measure customer satisfaction. The main part of this chapter describes the related concerns and challenges faced by the business regarding customer satisfaction. This chapter introduces the integrations of automated technology components, such as Automated Emotion Recognition System and Automated Text Content Analysis Tool. These components can be integrated into communication tools to solve the existing problems efficiently and improve the assessment of customer satisfaction.

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