CNN-Based Regional People Counting Algorithm Exploiting Multi-Scale Range-Time Maps with an IR-UWB Radar

2021 ◽  
pp. 1-1
Author(s):  
Runhan Bao ◽  
Zhaocheng Yang
Keyword(s):  
People Counting ◽  
Multi Scale ◽  
Uwb Radar ◽  
Scale Range ◽  
Counting Algorithm

A New and High-Accuracy People Counting Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 132-137
Author(s):  
Tao Hu ◽  
Hao Jia Luo ◽  
Ting Wang ◽  
Li Zhu
Keyword(s):  
Time Complexity ◽  
High Accuracy ◽  
Two Dimensional ◽  
People Counting ◽  
The People ◽  
Average Proportion ◽  
Counting Algorithms ◽  
Background Image ◽  
Counting Algorithm

The people counting algorithms are widely applied in many areas. The algorithms may have lower counting accuracy or higher time complexity. To improve that, a new and high-accuracy people counting algorithm is proposed in this paper. It analyzes a background image and series population original images to calculate an average proportion for one person and create a two-dimensional table. Then it uses the average proportion and the two-dimensional table to count the number of people in test images. And two thresholds are adopted to regulate accuracy in counting.

Bi-Directional Passing People Counting System Based on IR-UWB Radar Sensors

2018 ◽  
Vol 5 (2) ◽  
pp. 512-522 ◽  
Author(s):  
Jeong Woo Choi ◽  
Xuanjun Quan ◽  
Sung Ho Cho
Keyword(s):  
Counting System ◽  
People Counting ◽  
Radar Sensors ◽  
Uwb Radar

People Counting Based on an IR-UWB Radar Sensor

2017 ◽  
Vol 17 (17) ◽  
pp. 5717-5727 ◽  
Author(s):  
Jeong Woo Choi ◽  
Dae Hyeon Yim ◽  
Sung Ho Cho
Keyword(s):  
Radar Sensor ◽  
People Counting ◽  
Uwb Radar

scholarly journals MHW-PD: A robust rice panicles counting algorithm based on deep learning and multi-scale hybrid window

2020 ◽  
Vol 173 ◽  
pp. 105375
Author(s):  
Can Xu ◽  
Haiyan Jiang ◽  
Peter Yuen ◽  
Khan Zaki Ahmad ◽  
Yao Chen
Keyword(s):  
Deep Learning ◽  
Multi Scale ◽  
Rice Panicles ◽  
Counting Algorithm

A Wavelet-Based Method for Automatic Location of Peaks in Gamma-Ray Spectra

2014 ◽  
Vol 672-674 ◽  
pp. 1911-1919
Author(s):  
Cen Xiong ◽  
Shu Huan Liu ◽  
Yong Hong Li ◽  
Du Tang ◽  
Chao Hui He
Keyword(s):  
Gamma Ray ◽  
Analysis Method ◽  
Detection Ability ◽  
Time Frequency ◽  
Multi Scale ◽  
Promising Tool ◽  
Automatic Location ◽  
Time Frequency Localization ◽  
Scale Range ◽  
Localization Ability

Wavelet analysis method is a promising tool for analysis of gamma-ray spectra due to its time-frequency localization ability and multi-scale resolution feature. This paper describes a method to locate peaks in gamma-ray spectra based on continuous wavelet transform (CWT) of the spectrum using Marr wavelet. The theoretical basis of using Marr wavelet to detect Gaussian peaks was explained. A scale range, 1 to FWHMmax+2, was then suggested to perform CWT. Several novel criteria were used to discriminate real peaks from noise. The detection ability of this method was verified with some measured spectra. The results indicated that Marr wavelet did well in locating gamma-ray peaks.

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