Artificial intelligence (AI)-based security systems for monitoring and securing physical locations
Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.
1. An electronic monitoring platform communicatively coupled to a millimeter wave scanner, the electronic monitoring platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the electronic monitoring platform to:
receive, via the communication interface from the millimeter wave scanner, measurements corresponding to concealed objects associated with a plurality of individuals at a physical location;
perform cluster analysis on the measurements to determine groups of normal measurements corresponding to the concealed objects associated with the plurality of individuals;
receive, from the millimeter wave scanner and for an individual at the physical location, a measurement corresponding to concealed objects associated with the individual;
determine, based on the groups of normal measurements, that the measurement corresponding to the concealed objects associated with the individual is anomalous; and
based on the determining that the measurement corresponding to the concealed objects associated with the individual is anomalous, send one or more notifications to one or more security devices.
2. The electronic monitoring platform of claim 1 , wherein the measurements correspond to concealed metallic objects.
3. The electronic monitoring platform of claim 1 , wherein the computer-readable instructions that, when executed by the at least one processor, cause the electronic monitoring platform to perform the cluster analysis by causing performing one of:
hierarchical clustering;
centroid based clustering;
density based clustering;
distribution based clustering; and
combinations thereof.
4. The electronic monitoring platform of claim 1 , wherein the computer-readable instructions that, when executed by the at least one processor, cause the electronic monitoring platform to determine that the measurement is anomalous by causing determining that distances between the measurement and core points associated with the groups are greater than a threshold value.
5. The electronic monitoring platform of claim 1 , wherein the computer-readable instructions, when executed by the at least one processor, cause the electronic monitoring platform to:
receive, from one or more cameras, images captured at the physical location; and
perform, based on a convolutional neural network, image recognition analysis of the images to determine that the individual was at the physical location an anomalous number of times;
wherein the computer-readable instructions that, when executed by the at least one processor, cause the electronic monitoring platform to send the one or more notifications by causing sending the one or more notifications based on the determining that the individual was at the physical location an anomalous number of times.
6. The electronic monitoring platform of claim 1 , wherein the computer-readable instructions when executed by the at least one processor, cause the electronic monitoring platform to:
receive, from one or more cameras, videos captured at the physical location;
determine, based on the videos, gaits, paces, and postures associated with the plurality of individuals, wherein the performing the cluster analysis to determine the groups is further based on the determined gaits, paces, and postures; and
determine, based on the videos, a gait, a pace, and a posture of the individual, wherein the determining that the measurement corresponding to the concealed objects associated with the individual is anomalous is further based on the gait, the pace, and the posture of the individual.
7. The electronic monitoring platform of claim 6 , wherein the computer-readable instructions when executed by the at least one processor, cause the electronic monitoring platform to determine the gaits, the paces, and the postures associated with the plurality of individuals based on an AI model, wherein the AI model is based on one of:
a logistic regression model;
a decision tree model;
a random forest model;
a neural network;
a support vector machine; and
combinations thereof.
8. The electronic monitoring platform of claim 1 , wherein the measurements comprise one of:
sizes of the concealed objects;
locations of the concealed objects; and
combination thereof.
9. The electronic monitoring platform of claim 1 , wherein the one or more security devices comprises an actuator associated with a vault, and wherein the actuator causes the vault to lock based on receiving a notification.
10. The electronic monitoring platform of claim 1 , wherein the one or more security devices comprises an actuator associated with a shutter at a teller window, and wherein the actuator causes the shutter to activate based on receiving a notification.
11. A method comprising:
receiving, at an electronic monitoring platform and from a millimeter wave scanner, measurements corresponding to concealed objects associated with a plurality of individuals at a physical location;
performing cluster analysis on the measurements to determine groups of normal measurements corresponding to the concealed objects associated with the plurality of individuals;
receiving, from the millimeter wave scanner and for an individual at the physical location, a measurement corresponding to concealed objects associated with the individual;
determining, based on the groups of normal measurements, that the measurement corresponding to the concealed objects associated with the individual is anomalous; and
based on the determining that the measurement corresponding to the concealed objects associated with the individual is anomalous, sending one or more notifications to one or more security devices.
12. The method of claim 11 , wherein the measurements correspond to concealed metallic objects.
13. The method of claim 11 , wherein the performing the cluster analysis comprises performing one of:
hierarchical clustering;
centroid based clustering;
density based clustering;
distribution based clustering; and
combinations thereof.
14. The method of claim 11 , wherein the determining that the measurement is anomalous is based on determining that distances between the measurement and core points associated with the groups are greater than a threshold value.
15. The method of claim 11 , further comprising:
receiving, from one or more cameras, images captured at the physical location; and
performing, based on a convolutional neural network, image recognition analysis of the images to determine that the individual was at the physical location an anomalous number of times;
wherein the sending the one or more notifications is further based on the determining that the individual was at the physical location an anomalous number of times.
16. The method of claim 11 , wherein the measurements comprise one of:
sizes of the concealed objects;
locations of the concealed objects; and
combination thereof.
17. The method of claim 11 , wherein the one or more security devices comprises an actuator associated with a vault, and wherein the actuator causes the vault to lock based on receiving a notification.
18. The method of claim 11 , wherein the one or more security devices comprises an actuator associated with a shutter at a teller window, and wherein the actuator causes the shutter to activate based on receiving a notification.
19. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by a computer processor, causes a computing platform to:
receive, from a millimeter wave scanner, measurements corresponding to concealed objects associated with a plurality of individuals at a physical location;
perform cluster analysis on the measurements to determine groups of normal measurements corresponding to the concealed objects associated with the plurality of individuals;
receive, from the millimeter wave scanner and for an individual at the physical location, a measurement corresponding to concealed objects associated with the individual;
determine, based on the groups of normal measurements, that the measurement corresponding to the concealed objects associated with the individual is anomalous; and
based on the determining that the measurement corresponding to the concealed objects associated with the individual is anomalous, send one or more notifications to one or more security devices.
20. The non-transitory computer-readable medium of claim 19 , wherein the computer-readable instructions, when executed by the computer processor, causes the computing platform to perform the cluster analysis by causing performing one of:
hierarchical clustering;
centroid based clustering;
density based clustering;
distribution based clustering; and
combinations thereof.