Faster and more intelligent object detection by combining OpenCL and KR

被引：4

作者：

De Smedt, Floris ^{[1
,2
]}

Struyf, Lars ^{[1
]}

Beckers, Sander ^{[1
,3
]}

Vennekens, Joost ^{[1
,3
]}

De Samblanx, Gorik ^{[1
,3
]}

Goedeme, Toon ^{[1
,2
]}

机构：

[1] Assoc KU Leuven, Lessius Mechelen, Leuven, Belgium

[2] Katholieke Univ Leuven, Dept Elect Engn, Leuven, Belgium

[3] Katholieke Univ Leuven, Dept Comp Sci, Leuven, Belgium

来源：

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2014年 / 5卷 / 05期

关键词：

OpenCL; Data parallelism; Part based object detection; Knowledge representation;

D O I：

10.1007/s12652-013-0188-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a fast implementation of a robust object detector by using OpenCL. The use of fast object detection is of great use for a broad range of applications in multiple domains. OpenCL allows for scalability to more performant and different types of hardware, with minimal changes to the implementation. By using a GPU as execution device, we exploit the data parallelism opportunities of the algorithm. We also discuss the use of knowledge representation as a means to integrate expert knowledge into applications. This can be used both for faster processing by limiting the searching space, and for applications to work more autonomous by exploiting a higher level of intelligence.

引用

页码：635 / 643

页数：9

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