基于卷积神经网络的实时视频目标检测优化方法
Optimization Method for Real-Time Video Target Detection Based on Convolutional Neural Network
兰玉博1
作者信息
- 1. 郑州工业应用技术学院,河南郑州 451100
- 折叠
摘要
文章引入动态感兴趣区域(Dynamic Region of Interest,DROI)策略,提高基于区域卷积神经网络的快速目标检测(Faster Region-based Convolutional Neural Networks,Faster R-CNN)模型在实时视频目标检测任务中的性能.首先,分析Faster R-CNN;其次,提出一种基于DROI的优化方法,通过动态调整感兴趣区域以适应目标的运动和变化;最后,在MOT17数据集上进行实验,验证该优化方法的有效性.
Abstract
This paper introduces the Dynamic Region of Interest(DROI)strategy to improve the performance of Faster Region-based Convolutional Neural Networks(Faster R-CNN)model in real-time video target detection.First,Faster R-CNN is analyzed.Then,an optimization method based on DROI is proposed to adapt to the movement and changes of the target by dynamically adjusting the area of interest.Finally,experiments are carried out on the MOT17 data set to verify the effectiveness of the proposed optimization method.
关键词
卷积神经网络/动态感兴趣区域/目标检测/实时性Key words
convolutional neural network/dynamic region of interest/target detection/real-time引用本文复制引用
出版年
2024
NETL
NSTL
万方数据