According to a recent report published by Gartner, there will be 5.8 billion enterprise and automotive Internet of Things (IoT) endpoints in use by 2020, which represents a 21 percent increase over 2019. Gartner forecasts that more than 20 percent will consist of physical security devices, including video surveillance IP cameras, that allow to gather a significant amount of data, not only facial recognition but for a wide range of applications, not only security.
Historically the primary purview of most camera deployments was post-event analysis of security incidents, but now there are more and more IoT applications like people counting and dwell time, beside object recognition and classification using AI, to improve operations and customer service, and give end-users through an analytics dashboard a real-time view of their business to determine where more resources need to be devoted. 
A smart, IoT centred approach, eventually with a tight integration with physical IoT sensors, reduces the numerous false alarms generated throughout a typical day by various sensors. With a camera in place powered by underlying software that can decipher between humans, animals, vehicles and other objects, those alerts can be triaged and confirmed or denied quickly by security personnel, thus reducing alarm fatigue.

In addition to the wealth of data and advanced functionality that surveillance cameras can provide on their own, they can also be combined with other physical security systems, such as access control and intrusion detection that are also now making their way to IoT, to provide capabilities that once seemed impossible. In fact, there has been an increased emphasis in leveraging video combined with card readers to provide enhanced identity verification at various access points that require an additional layer of security. In this type of application, users can present their access credential to the reader as they normally would while a camera running facial recognition in the background verifies their identity.

Some organizations are also combining cameras with other IoT sensors to address the age-old problem of tailgating, particularly in locations where traditional solutions like turnstiles aren’t feasible. Access readers combined with AI-powered cameras can provide end-users with alerts when an unauthorized entry in these locations is detected so the incident can be quickly addressed. The threats posed by active shooters and data theft make this type of capability essential for organizations of all sizes.
IoT applications usually, offer more value when they incorporate video analytics. For instance, many IoT applications use Bluetooth beacons to transmit location data when they connect to a customer’s smartphone in a store. Integrating this data with video-analytics can provide more detailed information on details like gender and age group of the shoppers adding value to the insight.

Embedded sensors on buildings, cars, personnel or devices are able to provide data that’s either analysed on situ at the edge, or sent up to be crunched in the cloud in order to derive insight onto the “real” security situation – whereupon decisions can be made to activate alarms and send personnel. Integration between IoT platforms, their analytics layer, and Video Analytics systems enables a significant step into providing a more seamless experience for the end customer minimising the false alarms.