An Adaptive Moving Objects Detection Algorithm Based on Kernel Density Estimation

2013 ◽  
Vol 475-476 ◽  
pp. 983-986
Author(s):  
Man Hua ◽  
Yan Ling Li ◽  
Rui Chun Lin
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Video Processing ◽  
Moving Objects ◽  
Kernel Density ◽  
Detection Algorithm ◽  
Research Area ◽  
Global Threshold ◽  
Important Research Area ◽  
And Performance

The detection of moving objects are important research area for video surveillance and other video processing applications. In this paper, we propose an adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.

scholarly journals Robust Background Modeling with Kernel Density Estimation

2015 ◽  
Vol 11 (8) ◽  
pp. 13
Author(s):  
Man Hua ◽  
Yanling Li ◽  
Yinhui Luo
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Video Processing ◽  
Moving Objects ◽  
Kernel Density ◽  
Local Threshold ◽  
Global Threshold ◽  
Different Types ◽  
Temporal Persistence ◽  
And Performance

Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. In this paper, we proposed a novel adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.

scholarly journals Dockless Shared Bicycle Flow Control by Using Kernel Density Estimation Based Clustering

2021 ◽  
Vol 6 (3) ◽  
pp. 146-156
Author(s):  
Shang-Yuan Chen ◽  
Tzu-Tien Chen
Keyword(s):  
Flow Control ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Reverse Flow ◽  
Incentive Mechanism ◽  
Supply And Demand ◽  
Kernel Density ◽  
Research Area ◽  
The Incentive Mechanism ◽  
Shared Bicycle

Since dockless sharing bicycles have become an indispensable means of everyday life for urban residents, how to effectively control the supply and demand balance of bikes has become an important issue. This study aims to apply Kernel Density Estimation based (KDE-based) clustering analysis and a threshold-based reverse flow incentive mechanism to encourage the users of bicycles to adjust the supply and demand actively. And it takes Shanghai Jing’an Temple and its surroundings as the research area. Its practical steps include: (1) compilation and processing of the needed data, (2) application of KDE-based clustering, partitioning, and grading, and (3) incentives calculation based on dockless shared bicycle flow control system. The study finds that the generalization function of KDE-based clustering can be used to estimate the density value at any point in the study area to support the calculation of the incentive mechanism for bicycle reverse flow.

Estimating ADWF at Sewage Treatment Plants

2021 ◽  
Vol 6 (1) ◽  
pp. 1-22
Author(s):  
David de Haas ◽  
Stuart Ng ◽  
Nick Dahl ◽  
Dean Baulch
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Sewage Treatment ◽  
Kernel Density ◽  
Moving Averages ◽  
Sewage Treatment Plants ◽  
High Performing ◽  
Antecedent Precipitation ◽  
South Wales ◽  
And Performance

Estimating and understanding Average Dry Weather Flow (ADWF) is fundamental to the planning, design, and operation of sewage treatment plants (STPs). This paper reviewed methods for estimation of ADWF, in four general groups: Rainfall-based; Equivalent person (EP) based; Basic statistical (Percentiles); and ‘Novel’. The ‘Novel’ methods identified were: Histogram/ Mode; Antecedent Precipitation Index (API); Ratio of Short Term and Long-Term Moving Averages; K-means Clustering; Diurnal Profile Smoothing; and Kernel Density Estimation. EP-based methods were not considered useful because they shift the uncertainty from rainfall and/or flow data to population and/or loading data. The other methods were tested using datasets for two STPs of similar size (ADWF approximately 1.2 to 1.3 ML/d) in northern New South Wales, one of which is more prone to wet weather inflow/ infiltration (I/I). On balance of simplicity and performance against more complex methods, we recommend the Histogram/ Mode and/or the Percentile methods for routine reporting. For larger and more complex assignments (e.g., design projects, planning studies), it is recommended that one or more of the alternative high-performing methods described in this paper (e.g., Ratio of moving averages; Kernel Density Estimation) be employed for ADWF checks. Relatively large datasets (at least one year of daily flow totals) should be used and the results compared against the estimates from simpler methods.

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