Methods and systems for operating a moving platform to determine an identity of an unidentified target are disclosed. In an example method to determine the identity of the unidentified target, a first moving platform, configured with a first type of sensor, is caused to move to an area associated with the target. An attempt is made to determine, via the first moving platform and the first type of sensor, the identity of the target. Based on the attempt, a second moving platform, configured with a second type of sensor, is caused to move to the area. The identity of the target is determined via the second moving platform and the second type of sensor.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property.

A device that is configured to track an asset is disclosed. In one aspect, the device includes a radio module that generates proximity data that indicates a distance between the computing device and the device. The device includes a processor that compares the distance between the computing device and the device to a distance threshold. The processor determines that the distance between the computing device and the device satisfies the distance threshold. The processor arms the device. The device includes a motion sensor that generates motion data. The processor compares the motion data to a motion threshold. The processor determines that the motion data satisfies the motion threshold. The processor activates an alarm state.

An analytical recognition system is includes a video camera configured to capture video data of a subject and an antenna configured to capture mobile communication device data relating to a mobile communication device of the subject. The system further includes a data analytics module configured to: analyze the video data to determine at least one of a physical attribute or a movement attribute of the subject and generate; generate a first certainty match value based on the at least one of the physical attribute or the movement attribute of the subject; and perform a facial recognition analysis of the subject to obtain facial recognition data. The data analytics module is further configured to generate a second certainty match value based on the facial recognition data; generate a third certainty match value based on the mobile communication device data; and generate a combined certainty match value based on the first certainty match value, the second certainty match value, and the third certainty match value.

Systems and methods for surveillance of a facility including facility elements. The system includes a central computing unit providing a digital model of the facility providing topological or logical or functional relationships of the facility elements, surveillance sensors adapted for surveillance of a plurality of the facility elements and for generation of surveillance data, communication means for transmitting data from the surveillance sensors to the central computing unit, and state derivation means configured to analyse the surveillance data and derive a state of a respective facility element. The central computing unit is configured to record a state pattern by combining states of at least one facility element based on at least one relationship of the facility element provided by the facility model, provide a state pattern critical-noncritical classification model which considers relationships provided by the facility model, and perform a criticality-classification based on the relationship.

A method implemented by a system for security comprising an unmanned aerial vehicle (UAV) and an analytics device configured to communicate with a remote monitoring system and the UAV is provided. Media data from the UAV is received at an analytics device, where the media data includes surveillance information corresponding to a premises under surveillance. Security attributes associated with the premises are detected based at least in part on a first level of machine learning (ML) analysis performed on the media data. The media data are transmitted by the analytics device to the remote monitoring system for a second level of ML analysis based at least in part on the security attribute, where the first level of ML analysis is less computationally expensive compared to the second level of ML analysis, and the second level of ML analysis is performed at the remote monitoring system on the media data.

An aerial vehicle is programmed or configured to respond to reports of events or conditions within spaces of a facility. The aerial vehicle travels to a location of a reported event or condition and captures data using onboard sensors. The aerial vehicle independently determines whether the reported event or condition is occurring, or is otherwise properly addressed by resources that are available at the location, using images or other data captured by the onboard sensors. Alternatively, the aerial vehicle transmits a request for additional resources to be provided at the location, where necessary. A map of the location generated based on images or other data captured by the onboard sensors may be utilized for any purpose, such as to make one or more recommendations of products that are appropriate for use at the facility.

Systems and methods to automate both emergency identification and emergency response to minimize response time while optimizing the dispatching of different types of first responders are provided. The system may include an automation system. The automation system may receive a plurality of data elements from a plurality of sensors. The automation system may extract a set of features from the plurality of data elements. The automation system may identify an emergency situation based on the set of features and a set of rules. The automation system may assess the priority of the emergency situation based on a set of data elements associated with the emergency situation and a set of policies. The automation system may select a responder in response to the identified emergency situation. The automation system may dispatch the selected responder to an area related to the identified emergency situation.

An asset is identified in a predefined area. The type of asset is identified through video or wireless tag identification. A security policy associated with the type of asset is obtained. Entries in an asset log are recorded based on a location of the asset, an individual handling the asset, and/or actions taken by an individual with respect to the asset based on the security policy. A real-time notification is sent when the location, the handling, and/or any of the actions warrant a notification based on the security policy.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, a software platform and a smart electronic device. The smart electronic device is operable to interface with the software platform device to provide verification, receive alerts, and receive images, video and/or audio. The smart electronic device include one or more of a smart wearable device including smart watches, smart glasses, virtual reality devices, and augmented reality devices.