Cho Jaechan, Jung Yongchul, Kim Dong-Sun, Lee Seongjoo, Jung Yunho
School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea.
Korea Electronics Technology Institute, Seongnam-si 463-816, Korea.
Sensors (Basel). 2019 Jul 22;19(14):3217. doi: 10.3390/s19143217.
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.
大多数基于计算机视觉的运动目标检测（MOD）方法都局限于静态相机环境。然而，在先进驾驶辅助系统（ADAS）中，由于使用了移动相机，自身运动被添加到图像帧中。这导致图像帧中出现混合运动，使得难以对目标物体和背景进行分类。在本文中，我们提出了一种能够应对移动相机环境的高效MOD算法。此外，我们还展示了针对所提算法进行实时处理的硬件设计和实现结果。所提出的运动目标检测器使用硬件描述语言（HDL）进行设计，并使用基于FPGA的测试系统对其实时性能进行评估。实验结果表明，我们的设计比现有的MOD系统具有更好的检测性能。所提出的运动目标检测器采用13.2K逻辑切片、104个DSP48和163个BRAM实现，在200MHz的工作频率下能够支持30fps的实时处理。
