Modern technologies for hiding people's faces using object tracking based on YOLOv5 and DeepSort
The object of study is a system for automated blurring of human faces in video. This article provides a detailed overview of modern technologies and principles of tracking objects in video with assigning them unique elements. Since most video editors still leave most of the work to the user, it was...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Igor Sikorsky Kyiv Polytechnic Institute
2024-03-01
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| Series: | Adaptivni Sistemi Avtomatičnogo Upravlinnâ |
| Subjects: | |
| Online Access: | https://asac.kpi.ua/article/view/302439 |
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| Summary: | The object of study is a system for automated blurring of human faces in video. This article provides a detailed overview of modern technologies and principles of tracking objects in video with assigning them unique elements. Since most video editors still leave most of the work to the user, it was decided to optimize this process. The aim of this work is to reduce the time spent on the process of hiding human faces in video files. To achieve this goal, it is proposed to use a modern detector - the YOLO convolutional neural network and the DeepSORT object tracking algorithm, which uses classical approaches to filtering input data and predicting the position of an object in space, as well as a modern neural network capable of distinguishing between people's faces. As a result of this work, among free analogues on the Internet, the acceleration of face blurring was achieved up to 20%, which is a pretty good result.
Ref. 8, pic. 10, tabl. 3
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| ISSN: | 1560-8956 2522-9575 |