HUMAN DETECTION IN SURVEILLANCE VIDEO

2014 ◽  
Vol 28 (02) ◽  
pp. 1455003 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
LI-YUN WANG ◽  
CHIH-WEN SU
Keyword(s):  
Posterior Probability ◽  
Integrated Approach ◽  
Human Detection ◽  
Moving Object ◽  
Support Vector ◽  
Fourier Descriptor ◽  
Binary Classifier ◽  
Surveillance Video ◽  
Histogram Of Oriented Gradient ◽  
First Order

In this paper, we propose an integrated approach for human detection in surveillance video. In our approach, the moving object is extracted by background subtraction; and the background model is updated by the first-order recurrence filter. Then, two complementary features are extracted for moving object classification. They are contour-based description: Fourier descriptor and region-based description: histogram of oriented gradient. As the binary classifier (support vector machine) is able to provide the posterior probability, we effectively integrate two types of features to achieve better performance. Experimental results show that the proposed approach is effective and outperforms some existing technique.

Pedestrian Crowd Detection Based Unmanned Aerial Vehicle Infrared Imagery

2017 ◽  
Vol 873 ◽  
pp. 347-352
Author(s):  
Yong Hao Xiao ◽  
Hong Zhen
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Human Detection ◽  
Support Vector ◽  
Infrared Images ◽  
Histogram Of Oriented Gradient ◽  
Detection Approach ◽  
Aerial Vehicle ◽  
Geometric Filtering ◽  
Pedestrian Crowd

Human detection is a keyproblem in computer vision. Recently, some research has been focusing on the detection ofpedestrianusing infrared images. The infrared images have outstanding merit. It depends only on object's temperature, but not on color or texture. In this paper, the pedestrian crowd detection approach is proposed. The approach is compose of ROI blocks extraction and crowd block recognition. ROI blocks can be extracted with circle gradient operator and weighted geometric filtering. Crowd blocks are recognized by support vector machine, which combines histogram of oriented gradient and circle gradient. The experimental results show thatthe approach works effectively in different scenes.

scholarly journals A New Integrated Approach for Municipal Landfill Siting Based on Urban Physical Growth Prediction: A Case Study Mashhad Metropolis in Iran

2021 ◽  
Vol 13 (5) ◽  
pp. 949
Author(s):  
Salman Qureshi ◽  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Urban Growth ◽  
Integrated Approach ◽  
Physical Growth ◽  
Support Vector ◽  
Markov Chain Method ◽  
Physical Expansion ◽  
Hierarchy Process ◽  
The City

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.

scholarly journals Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections

2019 ◽  
Vol 1 (4) ◽  
pp. 1058-1083 ◽  
Author(s):  
Carl Leake ◽  
Hunter Johnston ◽  
Lidia Smith ◽  
Daniele Mortari
Keyword(s):  
Differential Equation ◽  
Support Vector Machines ◽  
Least Squares ◽  
Numerical Models ◽  
Linear Partial Differential Equation ◽  
Support Vector ◽  
Order Linear ◽  
First Order ◽  
Functional Connections ◽  
Vector Machines

Differential equations (DEs) are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. While there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the Theory of Functional Connections (TFC) with one based on least-squares support vector machines (LS-SVM). The TFC method uses a constrained expression, an expression that always satisfies the DE constraints, which transforms the process of solving a DE into solving an unconstrained optimization problem that is ultimately solved via least-squares (LS). In addition to individual analysis, the two methods are merged into a new methodology, called constrained SVMs (CSVM), by incorporating the LS-SVM method into the TFC framework to solve unconstrained problems. Numerical tests are conducted on four sample problems: One first order linear ordinary differential equation (ODE), one first order nonlinear ODE, one second order linear ODE, and one two-dimensional linear partial differential equation (PDE). Using the LS-SVM method as a benchmark, a speed comparison is made for all the problems by timing the training period, and an accuracy comparison is made using the maximum error and mean squared error on the training and test sets. In general, TFC is shown to be slightly faster (by an order of magnitude or less) and more accurate (by multiple orders of magnitude) than the LS-SVM and CSVM approaches.

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