[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.33851/JMIS.2019.6.4.203

Fast ROI Detection for Speed up in a CNN based Object Detection

Kim, Jin-Sung (Department of Electronic Engineering, Sun Moon University)
Lee, Youhak (Computer vision team, Chips&Media Inc.)
Lee, Kyujoong (Department of Electronic Engineering, Sun Moon University)
Lee, Hyuk-Jae (Department of Electrical and Computer Engineering, Seoul National University)

Publication Information

Journal of Multimedia Information System / v.6, no.4, 2019 , pp. 203-208 More about this Journal

Abstract

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 × 360 is 0.388msec.

Keywords

ROI detection; Noise; Image difference; Object detection;

Citations & Related Records

Reference

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