Robust sensitive-information de-identification framework based on relative-position estimation of objects in closed-circuit television videos

Abstract

Closed-circuit television (CCTV) videos provide important data for intelligent control systems; however, sensitive information such as human faces and vehicle license plates must be de-identified by a de-identification framework before such videos are used. Previous research has mainly focused on enhancing the performance of de-identification methods, and only a few are devoted to detecting sensitive information. To address this issue, this study proposes an artificial intelligence-based sensitive information de-identification framework. The framework detects objects and utilizes their structural information to estimate the relative position of sensitive data, followed by the de-identification process. To enhance the recall of the detection module, this study analyzes difficult-to-detect faces and license plates in CCTV videos and proposes relative position estimation methods. These methods minimize the possibility of sensitive information leakage while reducing the number of locations of sensitive information that need to be annotated. When evaluated using actual urban CCTV videos, the recall of the framework was 84.63%, which is 33.01% higher than that of the conventional algorithm.

Keywords

• CCTV video
• Relative-position estimation
• Sensitive information de-identification
• Structure information