Perception of Risks and Usefulness of Smart Video Surveillance Systems

Abstract

The number of video cameras in public places increases due to different reasons such as detecting dangers (e.g., thefts, robberies, terrorist attacks) and security breaches in crowds. The application of video surveillance systems is sometimes evaluated ambivalently; therefore, the presented study focuses on factors influencing the acceptance of a privacy-friendly, smart video surveillance system. Overall, 216 persons aged between 18 and 81 years participated in an online survey. In terms of the perceived usefulness, there are significant interactions of public spaces × gender and public spaces × time of day. In addition, the assessment of different privacy levels of a video surveillance system differ significantly in terms of perceived risk. Interestingly, men rate the risk concerning their own privacy significantly higher than women do. Participants rate the presented system as fairly useful and slightly risky for their own privacy. The findings of the presented exploratory study provide insight into how people perceive smart video surveillance. These findings have the potential to support the conditions of the use of smart video surveillance systems and to address the possibly affected individuals. smart video surveillance; intelligent video surveillance; machine learning; human pose estimation; privacy protection; online survey; exploratory study; time of day; public areas; gender; perceived privacy risk

A Machine Anomalous Sound Detection Method Using the lMS Spectrogram and ES-MobileNetV3 Network

“…The anomalous sound detection (ASD) system, which relies on the perception, processing, and decision making of sound signals, is garnering increasing enthusiasm in the field of monitoring, owing to its advantages in safeguarding user privacy [1], real-time capabilities [2], and adaptability to complex environments [3]. Currently, anomalous sound detection technology finds widespread application in various fields, including animal husbandry [4,5], machine condition monitoring [6,7], medical surveillance [8,9], and many other aspects.…”

Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions

“…While these modalities can partially or fully obfuscate an individual's identity, they require specialized hardware or cameras and can be expensive to be used by general population. Skeletons extracted from RGB camera streams using pose estimation algorithms provide a suitable solution of privacy protection over RGB and other types of cameras [15]. Skeleton tracking only focuses on body joints and ignores facial identity, full body scan or background information.…”