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https://doi.org/10.17559/TV-20171229024444

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

Lele Qin   ORCID icon orcid.org/0000-0003-3222-6453 ; School of Economic Management of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, China
Naiwen Yu ; Polytechnic College of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, China
Donghui Zhao ; School of Information Science and Engineering of Hebei University of Science & Technology, Shijiazhuang, Hebei, 050000, China

Puni tekst: engleski, pdf (2 MB) str. 528-535 preuzimanja: 417* citiraj
APA 6th Edition
Qin, L., Yu, N. i Zhao, D. (2018). Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video. Tehnički vjesnik, 25 (2), 528-535. https://doi.org/10.17559/TV-20171229024444
MLA 8th Edition
Qin, Lele, et al. "Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video." Tehnički vjesnik, vol. 25, br. 2, 2018, str. 528-535. https://doi.org/10.17559/TV-20171229024444. Citirano 15.05.2021.
Chicago 17th Edition
Qin, Lele, Naiwen Yu i Donghui Zhao. "Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video." Tehnički vjesnik 25, br. 2 (2018): 528-535. https://doi.org/10.17559/TV-20171229024444
Harvard
Qin, L., Yu, N., i Zhao, D. (2018). 'Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video', Tehnički vjesnik, 25(2), str. 528-535. https://doi.org/10.17559/TV-20171229024444
Vancouver
Qin L, Yu N, Zhao D. Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video. Tehnički vjesnik [Internet]. 2018 [pristupljeno 15.05.2021.];25(2):528-535. https://doi.org/10.17559/TV-20171229024444
IEEE
L. Qin, N. Yu i D. Zhao, "Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video", Tehnički vjesnik, vol.25, br. 2, str. 528-535, 2018. [Online]. https://doi.org/10.17559/TV-20171229024444

Sažetak
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.

Ključne riječi
convolutional neural network; deep learning technology; intelligent video; optical flow method

Hrčak ID: 199152

URI
https://hrcak.srce.hr/199152

Posjeta: 754 *