Crime type identification from video footage based on 3D convolutional neural network
| dc.contributor.author | Manteyeva N. | |
| dc.date.accessioned | 2024-12-19T12:03:58Z | |
| dc.date.available | 2024-12-19T12:03:58Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this thesis, we investigate the problem of crime classification given video footage from CCTV cameras. To do this, we will use 3D convolutional neural networks that first intended to be used for recognizing various actions. The unique feature of 3D convolutional neural networks lies in the fact that they capture both spatial and temporal dimensions by using 3D convolution, thus processing the motion data and its outlines. The system discussed in this thesis produces distinct channels for incoming data based on the input sketches. The final part includes representation that aggregates data from all channels. It is shown that the discussed architecture results a high accuracy in identifying the type of criminal offence shown in the underlying video footage. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1590 | |
| dc.language.iso | en | |
| dc.subject | crime, CCTV cameras, 3D convolutional neural networks, video footage | |
| dc.title | Crime type identification from video footage based on 3D convolutional neural network | |
| dc.type | Other |