Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video
In order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video devel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/293211 |
| _version_ | 1797207703742840832 |
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| author | Lele Qin Naiwen Yu Donghui Zhao |
| author_facet | Lele Qin Naiwen Yu Donghui Zhao |
| author_sort | Lele Qin |
| collection | DOAJ |
| description | In order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video development technology. First, the technical framework of intelligent video monitoring algorithm is divided into bottom (object detection), middle (object identification) and high (behaviour analysis) layers. The object detection based on background modelling is applied to routine real-time detection and early warning. The object detection based on object modelling is applied to after-event data query and retrieval. The related optical flow algorithms are used to achieve the identification and detection of abnormal behaviours. In order to improve the accuracy, effectiveness and intelligence of identification, the deep learning technology based on convolutional neural network is applied to enhance the learning and identification ability of learning machine and realize the real-time upgrade of intelligence video’s "brain". This research has a good popularization value in the application field of intelligent video technology. |
| first_indexed | 2024-04-24T09:27:08Z |
| format | Article |
| id | doaj.art-c84bec1270b148309f90ba18b345b42f |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:27:08Z |
| publishDate | 2018-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-c84bec1270b148309f90ba18b345b42f2024-04-15T14:44:15ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392018-01-0125252853510.17559/TV-20171229024444Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent VideoLele Qin0Naiwen Yu1Donghui Zhao2School of Economic Management of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, ChinaPolytechnic College of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, ChinaSchool of Information Science and Engineering of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, ChinaIn order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video development technology. First, the technical framework of intelligent video monitoring algorithm is divided into bottom (object detection), middle (object identification) and high (behaviour analysis) layers. The object detection based on background modelling is applied to routine real-time detection and early warning. The object detection based on object modelling is applied to after-event data query and retrieval. The related optical flow algorithms are used to achieve the identification and detection of abnormal behaviours. In order to improve the accuracy, effectiveness and intelligence of identification, the deep learning technology based on convolutional neural network is applied to enhance the learning and identification ability of learning machine and realize the real-time upgrade of intelligence video’s "brain". This research has a good popularization value in the application field of intelligent video technology.https://hrcak.srce.hr/file/293211convolutional neural networkdeep learning technologyintelligent videooptical flow method |
| spellingShingle | Lele Qin Naiwen Yu Donghui Zhao Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video Tehnički Vjesnik convolutional neural network deep learning technology intelligent video optical flow method |
| title | Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video |
| title_full | Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video |
| title_fullStr | Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video |
| title_full_unstemmed | Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video |
| title_short | Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video |
| title_sort | applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video |
| topic | convolutional neural network deep learning technology intelligent video optical flow method |
| url | https://hrcak.srce.hr/file/293211 |
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