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CPU-GPU heterogeneous implementations of depth-based foreground detection

Authors
Choi, YounchangKim, JinseongKim, JaehakChung, YongwhaPark, DaiheeLee, Sungju
Issue Date
25-2월-2018
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Keywords
computer vision; parallel processing; agriculture IT
Citation
IEICE ELECTRONICS EXPRESS, v.15, no.4
Indexed
SCIE
SCOPUS
Journal Title
IEICE ELECTRONICS EXPRESS
Volume
15
Number
4
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/77312
DOI
10.1587/elex.15.20170950
ISSN
1349-2543
Abstract
Video sensor data has been widely used in automatic surveillance applications. In this study, we present a method that automatically detects the foreground by using depth information. For real-time implementation, we propose a means of reducing the execution time by applying parallel processing techniques. In general, most parallel processing techniques have been used to parallelize each specific task efficiently. In this study, we consider a practical method to parallelize an entire system consisting of several tasks (i.e., low-level and intermediate-level computer vision tasks with different computational characteristics) by balancing the total workload between CPU and GPU. Experimental results with a pig monitoring application reveal that the proposed method can automatically detect the foreground using CPU-GPU heterogeneous computing platforms in real time, regardless of the relative performance between the CPU and GPU.
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Graduate School > Department of Computer and Information Science > 1. Journal Articles
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과학기술대학 (컴퓨터융합소프트웨어학과)
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