Abstract: Systems and methods are provided for intelligently monitoring environments, classifying objects within such environments, detecting events within such environments, receiving and propagating input concerning image information from multiple users in a collaborative environment, identifying and responding to situational abnormalities or situations of interest based on such detections and/or user inputs.

Abstract: Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

Abstract: Systems and methods are provided for generating and storing data snippets. A data file can be obtained from a data source through a plug-in interface. The data file can be played to a user through an application running on a computing device. An indication can be received from the user, through the application, to tag an entity depicted in the data file during playback of the data file. A snippet of the data file can be stored, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.

Abstract: Systems and methods are provided for generating and storing data snippets. A data file can be obtained from a data source through a plug-in interface. The data file can be played to a user through an application running on a computing device. An indication can be received from the user, through the application, to tag an entity depicted in the data file during playback of the data file. A snippet of the data file can be stored, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.

Abstract: An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.

Abstract: Systems and methods are provided for intelligently monitoring environments, classifying objects within such environments, detecting events within such environments, receiving and propagating input concerning image information from multiple users in a collaborative environment, identifying and responding to situational abnormalities or situations of interest based on such detections and/or user inputs.

Abstract: Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

Abstract: An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.

Abstract: An example method of determining geolocations of composite entities based on information retrieved from heterogeneous data sources comprises: identifying, by a computer system, an association of a first object and a second object with a composite object; receiving, from a first data source associated with the first object by an ontology, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the first object; receiving, from a second data source associated with the second object by the ontology, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the second object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the composite object and a corresponding time identifier.

Abstract: Methods, systems, and non-transitory computer readable media configured to display a geographical map overlaid with a marker layer comprising at least one marker; receive input from a user to change a zoom level of the geographical map from a first map scale to a second map scale; display the geographical map at the second map scale; and overlay the marker layer at the second map scale with the at least one marker at a second marker size. The second marker size is determined based on a correlation between the second map scale and the second marker size, in which (i) the second marker size is increased or decreased in the same direction as the second map scale when the second map scale is within a range from a low threshold point to a high threshold point, and (ii) a ratio change between two adjacent marker sizes is smaller than a ratio change between two corresponding adjacent map scales when the second marker size is outside the range from the low threshold point to the high threshold point.

Abstract: An example method of determining geolocations of composite entities based on information retrieved from heterogeneous data sources comprises: identifying, by a computer system, an association of a first object and a second object with a composite object; receiving, from a first data source associated with the first object by an ontology, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the first object; receiving, from a second data source associated with the second object by the ontology, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the second object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the composite object and a corresponding time identifier.

Abstract: Systems and methods are provided for analyzing data snippets. One or more snippets can be associated with a data object of an enterprise data platform. The one or more snippets can be organized based on metadata information associated with the one or more snippets. The organized snippets can be analyzed to determine an activity relating to an entity depicted in the one or more snippets.

Abstract: System and method for control area visualization according to certain embodiments. For example, a method includes: receiving object data from a plurality of data sources; identifying a plurality of first objects associated with a first group based at least in part on the object data, each first object of the plurality of first objects being associated with a geospatial location and a buffer area; determining a first control area associated with the first group based at least in part on the geospatial location and the buffer area of each first object of the plurality of first objects; and causing a presentation of the first control area on a display.

Abstract: In some embodiments, a method comprises obtaining a video stream of a portion of a geographic area, the video stream comprising a plurality of video frames, each of the plurality of video frames captured at a respective first time. Contextual metadata is obtained, the contextual metadata associated with one or more objects located in the portion of the geographic area at a second time, the second time being before each of the respective first times. The contextual metadata is inserted into one or more of the plurality of video frames, thereby causing the contextual metadata associated with the one or more objects to be overlaid on one or more corresponding portions of the one or more of the plurality of video frames.

Abstract: Methods, systems, and non-transitory computer readable media configured to display a geographical map overlaid with a marker layer comprising at least one marker; receive input from a user to change a zoom level of the geographical map from a first map scale to a second map scale; display the geographical map at the second map scale; and overlay the marker layer at the second map scale with the at least one marker at a second marker size. The second marker size is determined based on a correlation between the second map scale and the second marker size, in which (i) the second marker size is increased or decreased in the same direction as the second map scale when the second map scale is within a range from a low threshold point to a high threshold point, and (ii) a ratio change between two adjacent marker sizes is smaller than a ratio change between two corresponding adjacent map scales when the second marker size is outside the range from the low threshold point to the high threshold point.