利用局部特征匹配的运动小目标光流估计  

Optical Flow Estimation of Small Moving Targets Using Local Feature Matching

作　　者：陈华杰[1] 许琮擎 周枭 占俊杰 CHEN Huajie;XU Congqing;ZHOU Xiao;ZHAN Junjie(School of Automation Hangzhou Dianzi University,Hangzhou 310000 China)

机构地区：[1]杭州电子科技大学自动化学院,杭州310000

出　　处：《电光与控制》2024年第2期98-104,共7页Electronics Optics & Control

基　　金：浙江省科技计划项目(2022C01095)。

摘　　要：基于深度光流估计的动态背景运动小目标检测,为了保证小目标的检测性能,一般采用较少的下采样次数以维持较高的分辨率,但由此带来了较大的计算耗时。特征匹配是深度光流估计的一个核心处理环节,其耗时在光流估计整体耗时中的占比较大,且对下采样次数非常敏感。据此,提出一种基于局部特征匹配的快速光流估计算法:引入目标运动信息,缩小特征匹配的空间范围,减少待处理的数据量;设计分块局部匹配策略,引入批处理机制,避免出现逐点局部匹配策略数据处理耗时过大问题,实现算法加速。在此基础上,在光流估计获取的光流场上,采用CenterNet网络检测运动目标对应的光流异常区域。从光流估计耗时、检测精度等方面开展了实验验证,结果表明:针对运动小目标检测,分块特征匹配光流估计比全局特征匹配光流估计耗时减少约25%,目标检测性能相当。To ensure the performance of small moving target detection in dynamic backgrounds based on depth optical flow estimation fewer times of down sampling are generally adopted to maintain a high resolution which leads to large computational time consumption.Feature matching is a core processing link of depth optical flow estimation which takes up a large proportion of the overall time consumption of optical flow estimation and is very sensitive to the operation times of down sampling.Therefore a fast optical flow estimation algorithm based on local feature matching is proposed.The target motion information is introduced the spatial range of feature matching is narrowed and the amount of data to be processed is reduced.A block-based local matching strategy is designed and the batch processing mechanism is introduced to avoid the problem of large time consumption in data processing of the pointwise local matching strategy thus to accelerate the algorithm.Based on this CenterNet network is adopted to detect the optical flow anomaly areas corresponding to the moving target in the optical flow field obtained by optical flow estimation.Experimental verification is conducted from the perspectives of optical flow estimation time consumption and detection accuracy.The results show that as for small moving target detection the block-based feature matching optical flow estimation has about 25%less time consumption than the global feature matching optical flow estimation while the target detection performance is roughly equivalent.

关 键 词：运动小目标 动态背景 光流估计 局部特征匹配 光流异常区域检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]