scholarly journals Task-accurate performance modeling in SystemC for real-time multi-processor architectures

2006 ◽  
Author(s):  
M. Streubuhr ◽  
J. Falk ◽  
Ch. Haubelt ◽  
J. Teich ◽  
R. Dorsch ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Modeling ◽  
Processor Architectures ◽  
Accurate Performance

Performance Modeling of Real-Time Database Schedulers

1997 ◽  
pp. 251-276
Author(s):  
P. D. V. Stok ◽  
S. A. E. Sassen ◽  
M. P. Bodlaender ◽  
J. Wal ◽  
A. T. M. Aerts
Keyword(s):  
Real Time ◽  
Performance Modeling ◽  
Real Time Database

Comparison Of Real-Time Acousto-Optic SAR Processor Architectures

1987 ◽  
Author(s):  
K. T. Stalker ◽  
F. M. Dickey ◽  
P. A. Molley
Keyword(s):  
Real Time ◽  
Processor Architectures

scholarly journals Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

2021 ◽  
Vol 17 (02) ◽  
pp. 37
Author(s):  
Maryam A. Yasir ◽  
Yossra Hussain Ali
Keyword(s):  
Real Time ◽  
High Performance ◽  
Performance Metrics ◽  
Computation Time ◽  
Background Extraction ◽  
Accurate Performance ◽  
Real Time Applications ◽  
Conventional Models ◽  
Evaluation Approaches ◽  
Extraction Model

<p>In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.</p><p>In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. </p><p>In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.</p>

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