Privacy-preserving video fall detection using visual shielding information

Abstract

In recent years, fall detection, especially for the elderly living alone at home, is a challenge in the field of computer vision and pattern recognition. However, there is a concern of loss of privacy in intelligent visual surveillance. In order to solve the contradiction between security surveillance and privacy protection in vision-based fall detection methods, we propose a concept named visual shielding, which can be applied to eliminate visual information but not reduce monitoring function. In the preprocessing, the multilayer compressed sensing is used to compress video frames to achieve visual shielding effect. Then, object region is separated from shielded videos by the low-rank and sparse decomposition theory, based on which to extract motion trajectory features of the object via the dense trajectory algorithm. Finally, the fall detection issue is transformed into the sparse recognition problem of the signal. Experimental results on three public fall databases show that the specificity and sensitivity of the proposed method can be maintained at an ideal level, which has the dual advantages of high privacy protection and high recognition accuracy.