运动目标检测是物体检测领域的一个重要研究方向，在目标识别中有着至关重要的作用。针对传统运动检测方法精度不高、无法对运动目标进行检测，本文将深度卷积神经网络引入到运动目标光流检测中，将前后帧图像及目标光流场图像作为网络的输入，自适应地学习运动目标光流，并通过对网络放大架构的优化及网络的精简，同时采用数据增广等技术，设计出精度与实时性兼顾的目标物体光流检测网络。实验结果表明，本文方法在运动目标的光流场检测中有更好的表现，SS-sp和CS-sp网络相比原网络在检测精度上均提高了约5.0%，同时大幅减少了网络的运行时间，基本满足实时检测的要求。
基于深度卷积神经网络的运动目标光流检测方法
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通信作者： huangmzqb@163.com
出版日期：2018年8月1日
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参考文献
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基金项目：
江苏省研究生培养创新工程(SJLX16_0498)；江苏省政策引导类计划(产学研合作)-前瞻性联合研究项目(BY2016022-32)
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王正来, 黄敏, 朱启兵, 等. 基于深度卷积神经网络的运动目标光流检测方法[J]. 光电工程, 2018, 45(8): 180027.
