scholarly journals Scale-Adaptive KCF Mixed with Deep Feature for Pedestrian Tracking

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 536
Author(s):  
Yang Zhou ◽  
Wenzhu Yang ◽  
Yuan Shen
Keyword(s):  
Neural Network ◽  
Template Matching ◽  
Detection Algorithm ◽  
Important Research ◽  
Pedestrian Tracking ◽  
Histogram Of Oriented Gradient ◽  
Deep Feature ◽  
Kernel Correlation ◽  
Gradient Feature ◽  
Small Improvement

Pedestrian tracking is an important research content in the field of computer vision. Tracking is achieved by predicting the position of a specific pedestrian in each frame of a video. Pedestrian tracking methods include neural network-based methods and traditional template matching-based methods, such as the SiamRPN (Siamese region proposal network), the DASiamRPN (distractor-aware SiamRPN), and the KCF (kernel correlation filter). The KCF algorithm has no scale-adaptive capability and cannot effectively solve the occlusion problem, and because of many defects of the HOG (histogram of oriented gradient) feature that the KCF uses, the tracking target is easy to lose. For those defects of the KCF algorithm, an improved KCF model, the SKCFMDF (scale-adaptive KCF mixed with deep feature) algorithm was designed. By introducing deep features extracted by a newly designed neural network and by introducing the YOLOv3 (you only look once version 3) object detection algorithm, which was also improved for more accurate detection, the model was able to achieve scale adaptation and to effectively solve the problem of occlusion and defects of the HOG feature. Compared with the original KCF, the success rate of pedestrian tracking under complex conditions was increased by 36%. Compared with the mainstream SiamRPN and DASiamRPN models, it was still able to achieve a small improvement.

Detection of sharp objects using deep neural network based object detection algorithm

2020 ◽  
Author(s):  
R Kayalvizhi ◽  
S Malarvizhi ◽  
Siddhartha Dhar Choudhury ◽  
Anita Topkar ◽  
P Vijayakumar
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Deep Neural Network ◽  
Detection Algorithm

scholarly journals Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 262
Author(s):  
Chih-Yung Huang ◽  
Zaky Dzulfikri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Template Matching ◽  
Health Condition ◽  
Vibration Measurement ◽  
One Dimensional ◽  
Stamping Process ◽  
Metalworking Industry ◽  
Increasing Demand ◽  
Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function

2021 ◽  
Vol 342 ◽  
pp. 129982
Author(s):  
Xiaofang Pan ◽  
Zhe Zhang ◽  
Haien Zhang ◽  
Zhihuang Wen ◽  
Wenbin Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Recurrent Neural Network ◽  
Detection Algorithm ◽  
Attention Mechanism

scholarly journals Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Complex Network ◽  
Individual Variation ◽  
Deep Neural Network ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Electrocardiogram Signal ◽  
Sensitivity Specificity ◽  
Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

IDENTIFICATION OF COHERENT STRUCTURES IN TURBULENT SHEAR FLOWS WITH A FUZZY ARTMAP NEURAL NETWORK

1996 ◽  
Vol 07 (05) ◽  
pp. 559-568 ◽  
Author(s):  
J. FERRE-GINE ◽  
R. RALLO ◽  
A. ARENAS ◽  
FRANCE GIRALT
Keyword(s):  
Neural Network ◽  
Template Matching ◽  
Phase Relationship ◽  
Fuzzy Artmap ◽  
Turbulent Shear ◽  
Training Procedure ◽  
Full Data ◽  
The Core ◽  
Data Files ◽  
Number Of Classes

An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.

scholarly journals Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4390
Author(s):  
Tingli Xiang ◽  
Hongjun Wang
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Mobile Communication ◽  
Detection Algorithm ◽  
Base Station ◽  
Distributed Sensor ◽  
Mobile Communication Network ◽  
The Neural Network ◽  
Signal Coverage ◽  
5G Mobile Communication

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects.

scholarly journals Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 67927-67939 ◽  
Author(s):  
Mohd Usama ◽  
Belal Ahmad ◽  
Jiafu Wan ◽  
M. Shamim Hossain ◽  
Mohammed F. Alhamid ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Disease Risk ◽  
Feature Learning ◽  
Recurrent Connection ◽  
Deep Feature ◽  
Deep Feature Learning
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