Concerns regarding the deployment of crowd detection analytics range from processing power to accuracy to privacy. All can be addressed with the right solution.
Responsibly and reliably deploying a crowd detection system in public spaces is of paramount importance. You can’t have one without the other. Yet, many of the main concerns surrounding crowd counting have something to do with one or both of these ideas.
Accuracy, processing power and flexibility
Obviously, accuracy is required to deploy a successful crowd detection solution. Without it, there is no point in deploying one.
While advancements in algorithms — thanks to deep learning and other techniques — are helping on the accuracy and reliability front, one of the biggest concern when deploying crowd detection analytics is processing power and the suitability of the analytics in the environment where you want to deploy, according to Tom Hofer, Product Manager at Senstar
“To meet processing power requirements, GPU support
on the server can be used but the better approach is to use an embedded platform with pre-trained and customized datasets,” he said.
Crowd analytic tools must also be flexible enough to allow end users to set limits for any environment, at the levels they think are necessary for their space, explained Chris Bruce, MD of GlobalReach Technology
As with any analytic used in a public domain, privacy is a major concern. The use of face recognition software
by government organizations has already been banned in several places in the U.S. Earlier this year the European Commission had also considered banning the use of face recognition in public areas for up to five years — it later backtracked on this.
Today, “The COVID-19 pandemic is accelerating the use of digital technologies
across work life, in the home and in our social interactions. This creates new opportunities but also risks and challenges on a global scale,” according to Bjørn Skou Eilertsen, CTO of Milestone Systems
. “Innovations in technology should be celebrated, but we must acknowledge our role in developing new technologies responsibly.”
Since the very nature of crowd detection is to identify the number of people in a space, there are obvious concerns about personal and data privacy. Stuart Mills, COO and Chief Customer Officer at CrowdVision
, addressed how it is highly important to provide accurate real-time data whilst ensuring that the persons who are “in view” cannot be individually identified.
“CrowdVision solutions detect people in public spaces using either cameras that act as sensors, which are mounted directly overhead so that faces or individual attributes cannot be seen, or by deploying LiDAR sensors
, which detect the human form rather than any personalized characteristics,” he said.
For GlobalReach Technology, it emphasized that the purpose of its software is to give the venue control of setting their own thresholds by using anonymous data gathered from Wi-Fi networks
, and to alert them and give them insight to take proactive steps to manage a crowd.
“They can publish this information or show it on a digital screen to show the public and allow people to take their own preventative steps. This anonymous approach protects individual privacy and gives the venue the information it needs to manage crowding,” Bruce said.
Crowd analytics will only continue to advance as deep learning algorithms get smarter and analytics providers continue to innovate, and it couldn’t come at a better time. While we all watch and wait to see how the pandemic plays out and see what the lasting effects are on the way we interact with each other, use cases for crowd detection will grow.