A method for monitoring a dangerous situation in a space based on a frequency response pattern according to an embodiment includes loading, by a processing unit of a mobile terminal located in the arbitrary space, a setting value corresponding to an operational mode selected through control software installed on the mobile terminal, controlling, by the processing unit, a speaker of the mobile terminal to generate a frequency corresponding to the loaded setting value, receiving the frequency which was generated in the controlling and reflected in the space through a microphone of the mobile terminal, determining, by the processing unit, whether the dangerous situation occurred in the space by comparing the frequency response pattern with a preset reference value, and taking, by the processing unit, a follow-up action when it is determined that the dangerous situation occurred in the space by the determining.

This document describes techniques, apparatuses, and systems for batch size adjustment using latency-critical event recognition. The techniques described herein enable an electronic device (e.g., security camera) to determine the likelihood of an event of interest (e.g., latency-critical event) occurring in data (e.g., audio and/or video) captured by the electronic device. To make such a determination, the electronic device may switch upload modes to upload the data, using a different batch size to reduce latency, to another device for user access, based on the likelihood of an event of interest occurring in the data. In this way, the techniques, apparatuses, and systems for batch size adjustment using latency-critical event recognition provide an efficient way to provide all-day security monitoring.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for using a drone to monitor a community. The drone may include a processor and a storage device storing instructions that, when executed by the processor, cause the one or more processors to perform operations. The operations may include receiving an instruction to deploy based on a determination, by a community monitoring system that an event was detected at a property of the community, navigating towards the property along an initial navigation path, obtaining local monitoring system data from a local monitoring system of a property of the community, generating based on the local monitoring system data a navigational model that identifies a location of each of one or more surveillance objectives, determining an adjusted navigation path to a location of a surveillance objective of the one or more surveillance objectives, and navigating along the adjusted navigation path.

A system and method for intelligently evaluating and automatically mitigating detected security activities includes implementing an on-premise security device that detects a potential security activity at a property of a subscriber; establishing a security channel between the on-premise security device and a remote machine learning-based security module operating in a cloud computing environment if the potential security activity satisfies escalation criteria; automatically transmitting, via the security channel, sensor data from the on-premise security device to the remote machine learning-based security module; computing, by the remote machine learning-based security module, a threat severity inference based on the sensor data; deriving device control instructions based on the threat severity inference; transmitting, via the security channel, the device control instructions to the on-premise security device; and mitigating the potential security activity by executing the device control instructions at the on-premise device.

Described herein are systems and methods for generating a watch list of users who pose specific security threats to a store. The method can include retrieving, by a computer system from a data store, case files that document activity that poses a security threat by a user at the store, predicting, based on applying prediction models to the case files, future activity associated with the case files, determining threat scores for the case files based on the predicted future activity, ranking the case files into a candidate list from highest to lowest threat score, generating a watch list for the store that includes a subset of the ranked case files based on which case files pose a greatest current threat to the store, generating summary videos for each case file in the watch list, and transmitting the watch list and summary videos to a user device.

An analytical recognition system includes a video camera, an antenna, and a data analytics module. The video camera is configured to capture video data. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone camera. The antenna is configured to capture mobile communication device data. The data analytics module is configured to correlate the video data and the mobile communication device data to generate a profile of a person associated with the video data and the mobile communication device data. The profile has profile data including any one or a combination of the captured video data and the captured mobile communication device data.

An unmanned preview system that enables an unmanned preview of a building without requiring presence of an agent and can suppress damage or the like inside the building, wherein an unmanned preview system includes: a preview robot that is configured to be placeable inside the building and has portability; and a management device that is connected to the preview robot via a communication line and manages the preview robot. The preview robot includes an abnormality notifier that notifies the management device of an abnormality via the communication line when the abnormality having occurred inside the building is detected. The management device includes an abnormality annunciator that announces the abnormality to an administrator when it is notified of the abnormality by the abnormality notifier.

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.

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.

The present invention provides a processing apparatus (10) including an acquisition unit (11) that acquires a capture image capturing a movement means, an image analysis unit (12) that computes, based on the capture image, the number of persons each having an attribute satisfying a first criterion among persons inside the movement means, and the number of persons targeted for comparison, a computation unit (13) that computes a ratio of the number of persons satisfying the first criterion to the number of persons targeted for comparison, and an output unit (14) that outputs alert information when the ratio satisfies an alert condition.