为了在图像处理中快速地实现运动检测和相机自身运动方式估算，引入了基于生物视觉机制的Reichardt运动检测器模型 （EMD）和感受域模板。分析了Reichardt运动检测器模型的基本特性及其缺陷。为了克服模型上的主要缺陷，在应用中选择了一种优化模型，应用该模型可以得到较好的运动检测结果。同时，提出了基于苍蝇视觉系统的6个感受域模板，用以实现简单自身运动方式的估算，如相机自身的平移、旋转等。最后，在FPGA（Field Programmable Gate Array）平台上实现了相关的算法。实验结果表明，优化后的运动检测器可以快速地判断局部运动方向，感受域模板可实现在特定背景下的简单运动方式估算；对分辨率为256×256像素的输入图片，本设计中的FPGA系统可达到每秒350帧的处理速率，所产生的延时仅为0.25 μs，达到了快速处理的要求。此模型可应用于实时的机器视觉系统，如进行障碍物检测、整体运动方式估算、UAV /MAV的稳定控制等。

In order to realize high-speed motion detection and camera ego-motion estimation in image processing，an insect-inspired Reichardt motion detector (Elementary Motion Detector (EMD)) and receptive fields based on insects vision system are applied.The principal characteristics and drawbacks of the Reichardt model are analyzed.According to one of its main drawbacks，a modified model is selected which performs better in motion detection than the original method.Moreover，six templates of receptive field based on flys vision system are designed for simple ego-motion estimation，such as self-translation and self-rotation of the camera.Finally，the related algorithms are implemented on a FPGA (Field Programmable Gate Array) platform.The results of several typical experiments demonstrate，that the EMDs can detect local optical flow quickly and the receptive field templates enable simple motion estimation under specific backgrounds.The developed FPGA system is sufficient to deal with a video frame rate of 350 fps at 256×256 pixels resolution，the resulting time delay of the Reichardt model implementation is only 0.25 μs.This hardware can be applied to real-time computer vision systems，such as for obstacle detection，motion estimation，UAV/MAV’s stability control and so on.