Honeywell International Inc.

Ademco Inc.

Vit Libal

Valerie Guralnik

Pavel Vacha

Henry Chen

Michelle Raymond

Jan Jelinek

Tomas Brodsky

Yunting Lin

Liana Maria Kiff

Lalitha M Eswara

Jian Geng Du

Karel Macek

Marek Sikora

Jana Trojanova

Manuj Sharma

Graham Whiting

Kameswararao Belamkonda

Erwan Rivet

Axel Reichert

Ondrej Kopicka

Neil Garland

A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.

Systems and methods for detecting patterns within video content

Methods, devices, and systems for providing a standard operating procedure associated with a monitoring system of facility are described herein. One method includes determining a plurality of parameters associated with a facility having a monitoring system, accessing a library of SOP templates to determine a particular SOP template having a correlation with the plurality of parameters, allowing a user to create an SOP by customizing the particular SOP template, and associating the created SOP with the plurality of parameters.

Providing a standard operating procedure associated with a monitoring system of a facility

A system and approach using geofencing events for controlling temperature in a building for comfort of a user upon his or her arrival to the building. A decision may be made when to use a direct trigger by an incoming geofencing event and when to use a time of arrival prediction casted by an outgoing geofencing event. One or more geofences may be placed optimally based on the trigger decision and other aspects related to commuting patterns of the user.

Geofencing for thermostatic control

Generally discussed herein are methods and apparatuses that can reduce conflicts in an area including vehicles. An apparatus can include transceiver circuitry, and conflict detection circuitry to receive a position of a vehicle in a geographical area that is segmented into discrete cells, determine whether the position is situated in a cell of the cells that includes a polygonal area representing an extended restricted area within the cell, and the extended restricted area completely within the polygonal area, in response to a determination the position is situated within the polygonal area, determine whether the position is situated within the extended restricted area, and in response to a determination the position is situated within the extended restricted area, provide one or more signals to the transceiver circuitry to cause the transceiver circuitry to transmit an alert to the vehicle indicating that a conflict exists.

Vehicle conflict detection

Methods, devices, and systems for building space control are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to receive a recording of a gesture interaction with a virtual control element associated with a setting of a space in a building, analyze the recorded gesture for gesture characteristics, and modify the setting of the space based on the virtual control element and the gesture characteristics.

Building space control

Systems and methods for increasing an efficiency of an operator of a security system are discussed generally herein. A system can include a memory including ontology data saved thereon, the ontology data can define interrelationships between a scanner associated with access to a room of an area under surveillance, a camera with a field of view at least partially overlapping a footprint of the room, an identifier configured to be scanned by the scanner and associated with a person, and a security policy including one or more predefined conditions, which when satisfied, indicate when a security threat exists, the security policy includes a response an operator can perform if the conditions are satisfied, and the system can include a query module configured to receive a query and search the ontology data and temporal and spatial data associated with the area under surveillance in response to receiving the query.

Security system operator efficiency

Methods, devices, and computer-readable media for determining a deployment of an access control system are described herein. One method includes extracting a plurality of two-dimensional spaces from a building information model of a facility, determining a plurality of connections between the plurality of spaces, defining a zone of the facility, wherein the zone includes a subset of the plurality of spaces and a subset of the plurality of connections between the spaces, and associating an access reader with a particular connection of the subset of the plurality of connections located on a boundary of the zone.

Determining a deployment of an access control system

A method and apparatus are provided where the method includes detecting a plurality of events related to the activities of users within a security system, wherein the events are defined by a plurality of attributes, wherein at least one attribute is categorical, and wherein a data distance between events is a function of event attributes, evaluating the detected events using a density based anomaly detection method f(r), where r is a size of a neighborhood around a data point, comparing a value of the evaluated expression with a margin threshold value (msg(r)), and setting an alarm upon detecting that the value exceeds the threshold value.

System and method of anomaly detection with categorical attributes

Generally discussed herein are techniques, software, apparatuses, and systems configured for detecting an anomaly in a camera. In one or more embodiments a method can include: (1) determining a first property of a reference image, (2) determining a second property of a second image captured after the first image, wherein the second property is the same property as the first property, (3) comparing the first property to the second property, (4) determining a lens condition, a protective cover condition, an image sensor condition, a focus, or a field of view of the camera has changed since the first time in response to determining the first property is substantially different from the second property, or (5) sending an alert indicating that the lens condition, the protective cover condition, the image sensor condition, the focus, or the field of view of the camera has changed.

Detecting camera conditions to initiate camera maintenance

Devices, methods, and systems for monitoring access to a location are described herein. One or more method embodiments include determining data associated with an access event associated with a location, determining whether the access event is an anomalous access event using the data associated with the access event and a statistical model of data associated with a number of non-anomalous access events associated with the location, and assessing, if the access event is determined to be an anomalous access event, the anomalous access event. In various embodiments, assessing the anomalous access event includes at least one of determining an anomaly type associated with the anomalous access event, determining an anomaly classification confidence associated with the anomalous access event, determining an anomaly severity associated with the anomalous access event, and determining a reliability associated with the statistical model.

Monitoring access to a location

Methods, devices, and systems for detecting retail shrinkage using behavior analytics are described herein. The retail shrinkage may be due to, for example, sweethearting, although embodiments of the present disclosure are not so limited and can be used to detect other forms of retail shrinkage as well. One or more device embodiments include a memory, and a processor coupled to the memory. The processor is configured to execute executable instructions stored in the memory to receive data associated with behavior of an individual and use the data associated with the behavior of the individual to determine whether the behavior of the individual is irregular to detect retail shrinkage.

Detecting retail shrinkage using behavioral analytics

One or more embodiments include collecting data associated with a first access profile, collecting data associated with a second access profile, determining whether the data associated with the first access profile has a particular number of characteristics in common with the data associated with the second access profile, assigning a statistical access model associated with the second access profile to the first access profile based on the particular number of characteristics that the data associated with first access profile has in common with the data associated with the second access profile, and detecting an anomalous access event based on the statistical access model.

Acquiring statistical access models

A system and method includes obtaining and storing video frames from a series of video frames on a computer readable storage device, calculating probability estimates for target locations in each frame for targets in a constrained environment, and determining candidate target locations in each frame.

Vision based target tracking for constrained environments

A method and apparatus where the method includes detecting a plurality of events related to the activities of users within a security system wherein the events are defined by a plurality of attributes, wherein at least one attribute is categorical and wherein a data distance between events is a function of event attributes, evaluating the detected events using a density based anomaly detection method f(r), where r is a size of a neighborhood around a data point, comparing a value of the evaluated expression with a margin threshold value (msg(r)) and setting an alarm upon detecting that the value exceeds the threshold value.

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Prague, Czechia

Vit Libal