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Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems

1
School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea
2
Korea Electronics Technology Institute, Seongnam-si 463-816, Korea
3
Department of Information and Communication Engineering, Sejong University, Seoul 143-747, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3217; https://doi.org/10.3390/s19143217
Received: 18 June 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
(This article belongs to the Special Issue Perception Sensors for Road Applications)
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Abstract

Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz. View Full-Text
Keywords: ADAS; background subtraction; FPGA; moving object detection; optical flow estimation ADAS; background subtraction; FPGA; moving object detection; optical flow estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cho, J.; Jung, Y.; Kim, D.-S.; Lee, S.; Jung, Y. Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems. Sensors 2019, 19, 3217.

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