Real-time face detection in Full HD images exploiting both embedded CPU and GPU

Author

Chanyoung Oh ; Saehanseul Yi ; Youngmin Yi

Author_Institution

Sch. of Electr. & Comput. Eng., Univ. of Seoul, Seoul, South Korea

fYear

2015

fDate

June 29 2015-July 3 2015

Firstpage

1

Lastpage

6

Abstract

CPU-GPU heterogeneous systems have become a mainstream platform in both server and embedded domains with ever increasing demand for powerful accelerator. In this paper, we present parallelization techniques that exploit both data and task parallelism of LBP based face detection algorithm on an embedded heterogeneous platform. By running tasks in a pipelined parallel way on multicore CPUs and by offloading a data-parallel task to a GPU, we could successfully achieve 29 fps for Full HD inputs on Tegra K1 platform where quad-core Cortex-A15 CPU and CUDA supported 192-core GPU are integrated. This corresponds to 5.54x speedup over a sequential version and 1.69x speedup compared to the GPU-only implementations.

Keywords

embedded systems; face recognition; graphics processing units; parallel architectures; CUDA; GPU-only implementations; LBP based algorithm; Tegra K1 platform; data parallelism; embedded domains; embedded heterogeneous platform; full HD images; mainstream platform; multicore CPU; quad-core Cortex-A15; real-time face detection; server domains; task parallelism; Acceleration; Detectors; Face; Face detection; Graphics processing units; Kernel; Real-time systems; CPU-GPU heterogeneous platform; Face detection; Tegra K1; data-parallel; task-parallel;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo (ICME), 2015 IEEE International Conference on

Conference_Location

Turin

Type

conf

DOI

10.1109/ICME.2015.7177522

Filename

7177522