A Novel Intelligent Video Surveillance System Using Low-Traffic Scene-Preserving Video Anonymization

With the development of computer vision technology, intelligent video surveillance systems have been developed for automatic monitoring. However, the problem of personal information protection has also emerged. Existing systems attempted to solve this problem by anonymizing a video by, for example, sending only low-dimensional abstract information such as a person’s 2D pose or blurring a person’s face in the video before sending it to the central cloud server. However, these approaches failed to balance scene-preservation and traffic efficiency, because abstract information is too limited for preserving the entire scene, and video modification generates massive traffic. This article proposes a novel intelligent video surveillance system to overcome such limitations that preserves the scene information and generates minimal traffic through video anonymization. The proposed system reconstructs 3D human models and estimates segmentation masks to preserve a scene captured by a surveillance camera in its entirety. Parametric models represent 3D human models with several sets of parameters, and dictionary coding compresses the segmentation mask with a high compression ratio. The system follows the edge-cloud architecture, where the edge node extracts and transmits the scene information and the central cloud server generates the final anonymized video. We demonstrate the effectiveness of the proposed system by conducting experiments on processing time, scene preservation, and traffic efficiency. Our proposed system runs in real-time ( \(>\) 25fps) in a typical hardware setting and has a data compression ratio of more than 5,000 compared with raw data transfer while maintaining over 85% scene-preservation correlation with the original video.