摘要
为了解决深度学习技术无法满足用户对多张照片移动目标物体识别的需求,提出基于深度学习的多媒体移动物体检测技术.提出基于前馈神经网络的目标定位技术,设计目标定位模型,计算目标边界框的大致位置以及概率;对视频中的目标行为进行预测;利用卷积神经网络融合特征和自然语言搜索特征完成自然目标搜索.
In order to solve the problem that deep learning technology can not meet the user's need for moving object recognition in multiple photos,a multimedia mobile object detection technology based on deep learning is proposed.Firstly,a target location technology based on feedforward neural network is proposed,and a target location model is designed to calculate the approximate position and probability of the target boundary box,as well as the complete algorithm flow design.Secondly,based on the method of time-space fusion,the feature extraction of time-space feature fusion and the extraction of time information from 3D convolution network are used to predict the target behavior in video.Finally,by using natural language and extracting local features,using convolutional neural network fusion features and natural language search features to complete natural target search.
作者
李明东
卢彪
辛正华
房爱东
LI Mingdong;LU Biao;XIN Zhenghua;FANG Aidong(Suzhou University,College of Information Engineering,Suzhou 234000,China)
出处
《牡丹江师范学院学报(自然科学版)》
2019年第4期9-12,共4页
Journal of Mudanjiang Normal University:Natural Sciences Edition
基金
安徽省自然科学重点项目(KJ2019A1001)
2018教育部产学合作协同育人项目(201802203001)
宿州市科技攻关项目(SZ2018GG04)
2017年度宿州学院重点科研项目(2017yzd19)
宿州学院校级平台项目(2019ykf04)
宿州学院横向项目(2019hx011)
宿州学院基层教研室示范项目(2018jyssf113)
关键词
深度学习
目标检测
自然语言
deep learning
target detection
natural language