Crowd Monitoring: State-of-the-Art and Future Directions

Abstract

With the growing concerns over public safety, the importance of crowd monitoring is being realized by various security and event management agencies worldwide. Estimation of crowd dynamics can help such agencies in prevention of any unanticipated accidents or issues. Research on crowd monitoring has been underway since the past few decades. Conventional crowd monitoring systems mainly rely on computer vision approach. Due to predominant use of videos/ image sequences, the existing techniques may raise data privacy concerns. This has led to development of new crowd monitoring techniques which are privacy preserving and require minimum public participation. This paper aims to serve as a single and sufficient source of information to the concerned researchers on various aspects of crowd monitoring and also provide future directions which can be helpful in developing advanced crowd monitoring techniques.

Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features

“…This section reports the results of the experimental campaign carried out on some synthetic data sets to investigate the performance of the proposed SM identification procedure 2 . The outcome of a single illustrative test is first discussed for the purpose of providing an insight on the outlined strategy.…”

“…From an overall perspective, such highly cooperative multi-sensor systems are composed of a collection of spatially distributed static or (partially) dynamic smart camera-devices, capable of communicating over a wireless network, and then processing and fusing images of a scene from a variety of viewpoints into some form more useful than individual images [1]. Supported by the last advances in high performance embedded microprocessors, fully optimized computer vision algorithms and safe, fast and reliable communication protocols, VSNs are rapidly becoming an ubiquitous and strategic technology in industrial, rural, civil and domestic contexts, enabling vision-based interpretative applications as, for instance, events and crowd monitoring [2], dynamic intruders tracking [3], and assisted and autonomous driving [4].…”

Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

“…This implies that the system efficiency may drop in a dark environment since the images will likely be blurred. Again, the computer vision approach of object detection still has some challenges such as high installation cost, complexity in video analysis and processing, and privacy-related concerns [ 198 ]. A summary of location-based social distancing using computer vision is presented in Table 9 .…”

“…Furthermore, it has been noted that virtual reality promotes teamwork and a sense of togetherness without incurring the cost of traveling. Experts have opined that VR is a very useful tool in combating COVID-19 since it can effectively reduce people’s physical contacts yet provides an environment that appears as though the people are in their original interactions [ 197 , 198 ]. VR can be applied in the fight against COVID-19 especially in telemedicine, awareness campaign, and other activities that enhance the efficiency of health services such as medical training during the lockdown period.…”

A Crowd Monitoring Methodology based on the Analysis of the Electromagnetic Spectrum

“…n the last decade, there has been a growing interest in security applications and, in particular of techniques to crowd density estimations in critical areas such as airports, stadiums, supermarkets, and other aggregation areas (Singh et al, 2020;Rahman et al, 2006;Ohmann et al, 2006;Oeimiane et al, 2020;Jeong et al, 2013). The most popular techniques aimed at detect crowds are based on image processing (Paulsen et al, 1997;Velastin, 1994;Marana, 1997;Jarndal & Alnajjar, 2018), but they require video cameras, and infrastructures to correctly work (Paulsen et al, 1997).…”