- Volume 19 Issue 9
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A fixed-point implementation and performance analysis of EGML moving object detection algorithm
EGML 이동 객체 검출 알고리듬의 고정소수점 구현 및 성능 분석
An, Hyo-sik;Kim, Gyeong-hun;Shin, Kyung-wook
- Received : 2015.06.24
- Accepted : 2015.07.31
- Published : 2015.08.20
An analysis of hardware design conditions of moving object detection (MOD) algorithm is described, which is based on effective Gaussian mixture learning (EGML). A simulation model of EGML algorithm is implemented using OpenCV, and the effects of some parameter values on background learning time and MOD sensitivity are analyzed for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-widths of parameters. The proposed fixed-point model of the EGML-based MOD uses only half of the bit-width at the expense of the loss of MOD performance within 0.5% when compared with floating-point MOD results.
moving object detection;MOD;Gaussian mixture model;EGML;background learning
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Grant : 사물인터넷 기반 영상보안용 초저전력 SoC 핵심 IP 기술 개발