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Proceedings Paper

Heterogeneous computing for a real-time pig monitoring system
Author(s): Younchang Choi; Jinseong Kim; Jaehak Kim; Yeonwoo Chung; Yongwha Chung; Daihee Park; Hakjae Kim
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Paper Abstract

Video sensor data has been widely used in automatic surveillance applications. In this study, we present a method that automatically detects pigs in a pig room by using depth information obtained from a Kinect sensor. For a 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 a specific task. In this study, we consider parallelization of an entire system that consists of several tasks. By applying a scheduling strategy to identify a computing device for each task and implementing it with OpenCL, we can reduce the total execution time efficiently. Experimental results reveal that the proposed method can automatically detect pigs using a CPU-GPU hybrid system in real time, regardless of the relative performance between the CPU and GPU.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431O (19 June 2017); doi: 10.1117/12.2280236
Show Author Affiliations
Younchang Choi, Korea Univ. (Korea, Republic of)
Jinseong Kim, Korea Univ. (Korea, Republic of)
Jaehak Kim, Korea Univ. (Korea, Republic of)
Yeonwoo Chung, Korea Univ. (Korea, Republic of)
Yongwha Chung, Korea Univ. (Korea, Republic of)
Daihee Park, Korea Univ. (Korea, Republic of)
Hakjae Kim, ClassAct (Korea, Republic of)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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