# Perception of Risks and Usefulness of Smart Video Surveillance Systems
## 1 Introduction
### Smart Video Surveillance Using Digital Skeletons  
Conventional video surveillance is a topic that is already present in many cities. With the increasing number of installed video cameras [1], retaining an overview of data becomes increasingly difficult. At this point, smart video surveillance receives more attention, especially as assisting systems for video operators. In recent years, many technological advances have resulted in interesting application possibilities for smart video surveillance systems. Security services already started to rely on unmanned aerial vehicles (UAV) for short-term use cases such as recon flights at disaster scenarios, which opens up new possibilities, as initial work has already demonstrated their suitability for the detection and localization of persons and objects [15; 16; 17]. The use of facial recognition for tracking or recognition in classical video surveillance setups has also experienced significant improvement in the past [18; 19; 20] as well as attribute-based person re-identification [21; 22]. Even the field of action recognition has also seen a variety of methods emerge in the near past, promising ever better accuracies and real-time processing, which is essential for serious applications [23; 24]. The aforementioned aspects appear to be solutions for existing problems, such as the detection of salient behaving or hurt pedestrians, but the use of such methods is restricted by tough standards, especially in Europe or other Western countries. The General Data Protection Regulation (GDPR) [25] is a regulation in EU law on data protection and privacy in the European Union (EU) and the European Economic Area (EEA), which strongly limits the application of existing methods for smart video surveillance systems. In addition, the Regulation (EU) 2019/947 [26] restricts and regulates the application of UAVs for civil and public use in Europe [27]. Therefore, the urgent need for privacy-friendly methods and approaches in ground-related video surveillance emerges. There are different ways to deal with the legal limitations, such as the application of synthetic data for tracking or human pose estimation [28; 29; 30], and the utilization of human poses for behavior analysis of pedestrians, meaning that the analysis of pedestrians solely relies on motion and body-structure information. Especially the last one is an approach that has been successfully brought into practice in a project within the German city of Mannheim [2; 3; 10]. The presented work examines the factors influencing acceptance toward smart video surveillance, in particular that of using digital skeletons for behavior analysis [2; 10]. The aim of such algorithms is to recognize crimes and atypical behaviors and movements of pedestrians (including tripping and collapsing) based on patterns in a live transmission of the video material. The typical setup proposed in the literature [2; 12; 13; 31; 32] shares the same structure: First, the system determines the spatial location of limbs for all human bodies within the given scene. This is completed either by locating the pedestrians first and then determining the position of the limbs (top-down) or by determining the position of the limbs first (bottom-up), and then associating them to corresponding persons. These information are then collected over time in order to extract spatio-temporal features for the following step. The collected information is used to create the digital skeletons, which are typically also referred to as human poses. The most common way to extract such skeletons is to use machine learning methods. This basic algorithmic approach can be included into a productive overall system, which could look as follows: The smart video analysis is part of a video management system (VMS), which is responsible for acquiring live video material from, e.g., IP cameras. The image material is then forwarded to the smart subsystem, processed, and the results are returned to the VMS.