[1]缪宇杰,吴智钧,宫 婧.基于3D 卷积的视频错帧筛选方法[J].计算机技术与发展,2018,28(05):179-181.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
　MIAO Yu-jie,WU Zhi-jun,GONG Jing.A Wrong Temporal-order Frames Identification Method Based on 3D Convolution[J].,2018,28(05):179-181.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
点击复制

基于3D 卷积的视频错帧筛选方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
179-181
栏目:
应用开发研究
出版日期:
2018-05-10

文章信息/Info

Title:
A Wrong Temporal-order Frames Identification Method Based on 3D Convolution
文章编号:
1673-629X(2018)05-0179-03
作者:
缪宇杰1 ; 吴智钧1 ; 宫 婧2
1. 南京邮电大学 物联网学院,江苏 南京 210003;
2. 南京邮电大学 理学院,江苏 南京 210003
Author(s):
MIAO Yu-jie 1 ; WU Zhi-jun 1 ; GONG Jing 2
1. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. School of Science,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
无监督学习; 卷积神经网络; 错帧筛选; 3D 卷积
Keywords:
unsupervised learning; CNN; frame-sequence identification; 3D convolution
分类号:
TP301
DOI:
10.3969/ j. issn.1673-629X.2018.05.040
文献标志码:
A
摘要:
为了提取更好的视频特征,提高训练精准度,提出了一个基于 CNN(convolutional neural network,卷积神经网络)的错帧筛选模型。 所谓错帧,是指在时间上乱序的帧序列,相反,有序帧是指遵守时间顺序的帧序列。 其目标是从若干组帧序列中,筛选出顺序错误的一组帧序列。 采用无监督学习的方法来训练模型,因此不需要依赖有标签的数据集。 基于这个模型的目标以及无标签的训练方式,采用了一个多分支的 CNN 结构,并且是端到端的。 其输入的若干组帧序列从视频中采样获得,分别进行 3D 卷积编码后,能够提取出每组帧序列在时间和空间上的特征。 为了找出帧顺序有误的一组序列,该模型对每组帧序列进行对比,找出它们之间的共同规则,从而筛选出违背此规则的那一组序列。 在 UCF101 数据集上的实验结果证实了该方法的有效性,错帧筛选的准确率高。
Abstract:
In order to extract better video features and improve training accuracy,we propose a model of wrong temporal-ordered frames based on CNN (convolutional neural network),whose task is identifying the sequence of wrong temporal-ordered frames from several sequences of frames. The sequence of wrong frames is wrong temporal-ordered while the right sequence is temporal-ordered. Unsupervised video representation learning is applied to train this model,therefore labeled data sets are unnecessary. Based on the task and no se-
mantic labels,a multi-branched CNN structure is implemented which is learned end-to-end. As the model input,the sequences of frames are sampled from one video. Then,these sequences of frames are encoded with the method of 3D convolution to extract the temporal and spatial features of each sequence of frames. To find out the sequence of frames with wrong temporal-order,the model has to compare all the inputs,analyze the regularities among them,and identify the one with irregularities. The experiments on UCF101 dataset verify the effectiveness of the proposed method,and the accuracy of this model is high.

相似文献/References:

[1]崔凤焦.表情识别算法研究进展与性能比较[J].计算机技术与发展,2018,28(02):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
　CUI Feng-jiao.Ｒesearch and Performance Comparison of Facial Expression Ｒecognition Algorithm[J].,2018,28(05):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
[2]张丹丹,李雷. 基于PCANet-RF的人脸检测系统[J].计算机技术与发展,2016,26(02):31.
　ZHANG Dan-dan,LI Lei. Face Detection System Based on PCANet-RF[J].,2016,26(05):31.
[3]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(05):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[4]郭子琰,舒心,刘常燕,等.基于ReLU 函数的卷积神经网络的花卉识别算法[J].计算机技术与发展,2018,28(05):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
　GUO Ziyan,SHU Xin,LIU Changyan,et al.A Recognition Algorithm of Flower Based on Convolution Neural Network with ReLU Function[J].,2018,28(05):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
[5]吴玉枝,吴志红,熊运余.基于卷积神经网络的小样本车辆检测与识别[J].计算机技术与发展,2018,28(06):1.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
　WU Yu-zhi,WU Zhi-hong,XIONG Yun-yu.Vehicle Detection and Recognition of a Few Samples Based on Convolutional Neural Network[J].,2018,28(05):1.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
[6]李相桥,李晨,田丽华,等.卷积神经网络并行训练的优化研究[J].计算机技术与发展,2018,28(08):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
　LI Xiang-qiao,LI Chen,TIAN Li-hua,et al.Research on Optimization of Parallel Training for Convolution Neural Network[J].,2018,28(05):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
[7]邓宗平,赵启军,陈虎. 基于深度学习的人脸姿态分类方法[J].计算机技术与发展,2016,26(07):11.
　DEND Zong-ping,ZHAO Qi-jun,CHEN Hu. Face Pose Classification Method Based on Deep Learning[J].,2016,26(05):11.
[8]河海大学 计算机与信息学院,江苏 南京 0098.卷积网络的无监督特征提取对人脸识别的研究[J].计算机技术与发展,2018,28(06):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
　DU Bai-sheng.Research on Unsupervised Feature Extraction Based on Convolutional Neural Network for Face Recognition[J].,2018,28(05):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
[9]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(05):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[10]戴晓薇,赵启军. 基于回归的指纹方向场估计[J].计算机技术与发展,2017,27(01):1.
　DAI Xiao-wei,ZHAO Qi-jun. Fingerprint Orientation Field Estimation Based on Regression[J].,2017,27(05):1.
[11]蒋文杰,罗晓曙*,戴沁璇.一种改进的生成对抗网络的图像上色方法研究[J].计算机技术与发展,2020,30(07):56.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 013]
　JIANG Wen-jie,LUO Xiao-shu*,DAI Qin-xuan.Research on an Improved Method of Generative Adversarial Networks Image Coloring[J].,2020,30(05):56.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 013]

更新日期/Last Update: 2018-07-20