Name
Abstract | ||
|---|---|---|
Accurate background subtraction is an essential tool for high level computer vision applications. However, as research continues to increase the accuracy of background subtraction algorithms, computational efficiency has often suffered as a result of increased complexity. Consequentially, many sophisticated algorithms are unable to maintain real-time speeds with increasingly high resolution video inputs. To combat this unfortunate reality, we propose to exploit the inherently parallelizable nature of background subtraction algorithms by making use of NVIDIA's parallel computing platform known as CUDA. By using the CUDA interface to execute parallel tasks in the Graphics Processing Unit (GPU), we are able to achieve up to a two orders of magnitude speed up over traditional techniques. Moreover, the proposed GPU algorithm achieves over 8x speed over its CPU-based background subtraction implementation proposed in our previous work [1]. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-03801-4_55 | ADVANCES IN VISUAL COMPUTING, ISVC 2018 |
Keywords | Field | DocType |
Graphics Processing Unit (GPU), Non-parametric, Background subtraction, CUDA, NVIDIA, Parallel programming | Parallelizable manifold,Background subtraction,Computer vision,CUDA,Computer science,Nonparametric statistics,Exploit,Computational science,Artificial intelligence,Graphics processing unit,Speedup | Conference |
Volume | ISSN | Citations |
11241 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Porr | 1 | 0 | 0.68 |
| James Easton | 2 | 0 | 0.68 |
| Alireza Tavakkoli | 3 | 168 | 15.97 |
| Donald A. Loffredo | 4 | 7 | 2.83 |
| Sean Simmons | 5 | 0 | 1.01 |