Abstract: A method for alarm verification comprises obtaining an alarm indication associated with a potential alarm triggered at a monitored site, obtaining media data captured by at least one media device, executing a machine learning model to conduct a content analysis of the media data and generate content metadata based thereon, obtaining sensor data acquired by at least one sensor, correlating the content metadata with the sensor data to determine whether an event corresponding to an alarm condition has occurred at the monitored site, in response to detecting occurrence of the event, determining that the alarm indication relates to a security issue and causing a first action to be performed to mitigate the security issue, and, in response to detecting that the event failed to occur, determining that the alarm indication relates to an operational issue and causing a second action to be performed to mitigate the operational issue.

Abstract: A graphical user interface to be displayed on a computing device associated with a video surveillance system is automatically configured to create links between cameras of the video surveillance system. An association between nearby cameras is obtained by identifying an object in a video feed of one of the cameras, and re-identifying the same object in another video feed of another one of the cameras. If the re-identification of the object takes place within a predetermined time period, it can be assumed that the object has moved from the field of view of the first camera into the field of view of the other camera. As a result, a user interface element resulting in a switch from the video feed of the first camera to the video feed of the other camera is configured to be superimposed on the video from the first camera.

Abstract: An example method of operating a computing apparatus, which comprises: obtaining, via a graphical user interface, user input indicative of a query relating to a video image of a scene captured by a camera; accessing an object stream data store comprising a plurality of object-based metadata records, each object-based metadata record of the plurality of object-based metadata records associated with a corresponding object depicted in video images captured by the camera and comprising at least one object stream comprising (i) an object identifier (ID) and (ii) one or more object attributes associated with the corresponding object, the object stream data store comprising aggregated identification information for video images in which the corresponding object was detected; identifying, based on the query, an object of interest from amongst objects depicted in the video images captured by the camera, the object of interest associated with a particular object-based metadata record; obtaining, from the object stream,

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: Methods, systems, and computer-readable media for producing a privacy-protected video stream are described herein. A request to display a live video stream of a camera is received. The live video stream is received in real-time and comprises a plurality of live image frames from the camera. An archived video stream of the camera is accessed in a data repository. A plurality of archived image frames of the archived video stream is processed to generate a background model comprising imagery common to multiple ones of the plurality of archived image frames. The plurality of archived image frames occurring in time prior to the request to display the live video stream. The privacy-protected video stream is processed in real-time. The privacy-protected video stream is output for display.

Abstract: A system for causing a searching of a data source of surveillance records; it has a processor; and memory comprising program code that, when executed by the processor, cause the processor to obtain an input query, the input query comprising natural language; provide the input query to a large language model (LLM), for translating the natural language into a structured data source query based on searchable categories associated with the surveillance records stored in the data source; obtain, from the LLM, a structured data source query based on the input query; transmit the structured data source query to the data source to perform a search of the data source in accordance with the structured data source query to identify at least one query result; and receive the at least one query results; a method of use thereof.

Abstract: A method of operating a computing apparatus, which comprises: accessing a plurality of temporal metadata datasets, each of the plurality of temporal metadata datasets associated with a video image frame of a scene and comprising (i) identification information for that video image frame; (ii) an object identifier (ID) for each of one or more objects detected in that video image frame; and (iii) one or more object attributes associated with each of the one or more objects detected in that video image frame; for a particular object having an object ID, identifying a subset of temporal metadata datasets in the plurality of temporal metadata datasets comprising an object ID that matches the object ID of the particular object, and processing the subset of temporal metadata datasets to create an object-based metadata record for the particular object, the object-based metadata record for the particular object comprising (i) the object ID; (ii) one or more object attributes associated with the particular object; and (

Abstract: Producing a privacy-protected video clip in a video management system includes retrieving a selected video clip constituting at least a portion of a stored video stream; obtaining segments of the video clip which are spaced apart in time and have a total length equal to a defined time period; combining the obtained segments of the video clip to form a background training clip; and processing the background training clip, or the separate segments, to produce a background model. The video clip is processed to produce a privacy-protected video clip, such that, for each image the video clip, the processing includes performing background subtraction, using the background model, to define foreground regions, and obscuring the defined foreground regions.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: Methods, systems, and computer-readable media for producing a privacy-protected video stream are described herein. A request to display a live video stream of a camera is received. The live video stream is received in real-time and comprises a plurality of live image frames from the camera. An archived video stream of the camera is accessed in a data repository. A plurality of archived image frames of the archived video stream is processed to generate a background model comprising imagery common to multiple ones of the plurality of archived image frames. The plurality of archived image frames occurring in time prior to the request to display the live video stream. The privacy-protected video stream is processed in real-time. The privacy-protected video stream is output for display.

Abstract: Methods, systems, and computer-readable media for producing a privacy-protected video stream are described herein. A request to display a live video stream of a camera is received. The live video stream is received in real-time and comprises a plurality of live image frames from the camera. An archived video stream of the camera is accessed in a data repository. A plurality of archived image frames of the archived video stream is processed to generate a background model comprising imagery common to multiple ones of the plurality of archived image frames. The plurality of archived image frames occurring in time prior to the request to display the live video stream. The privacy-protected video stream is processed in real-time. The privacy-protected video stream is output for display.

Abstract: Producing a privacy-protected video clip in a video management system includes retrieving a selected video clip constituting at least a portion of a stored video stream; obtaining segments of the video clip which are spaced apart in time and have a total length equal to a defined time period; combining the obtained segments of the video clip to form a background training clip; and processing the background training clip, or the separate segments, to produce a background model. The video clip is processed to produce a privacy-protected video clip, such that, for each image the video clip, the processing includes performing background subtraction, using the background model, to define foreground regions, and obscuring the defined foreground regions.

Abstract: Producing a privacy-protected video clip in a video management system includes retrieving a selected video clip constituting at least a portion of a stored video stream; obtaining segments of the video clip which are spaced apart in time and have a total length equal to a defined time period; combining the obtained segments of the video clip to form a background training clip; and processing the background training clip, or the separate segments, to produce a background model. The video clip is processed to produce a privacy-protected video clip, such that, for each image the video clip, the processing includes performing background subtraction, using the background model, to define foreground regions, and obscuring the defined foreground regions.

Abstract: Producing a privacy-protected video clip in a video management system includes retrieving a selected video clip constituting at least a portion of a stored video stream; obtaining segments of the video clip which are spaced apart in time and have a total length equal to a defined time period; combining the obtained segments of the video clip to form a background training clip; and processing the background training clip, or the separate segments, to produce a background model. The video clip is processed to produce a privacy-protected video clip, such that, for each image the video clip, the processing includes performing background subtraction, using the background model, to define foreground regions, and obscuring the defined foreground regions.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: In some embodiments, an apparatus includes a memory and a processor. The processor is configured to receive a set of images associated with a video recorded by a moving or a non-moving camera. The processor is configured to detect a structure of a region of interest from a set of regions of interest in an image from the set of images. The processor is configured to classify the structure into a geometric class from a set of predefined geometric classes using machine learning techniques. The processor is configured to alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected. The processor is configured to send the altered image to a user interface or store the altered image in a standardized format.

Abstract: In a method for locating patterns of movement in a first digital video signal, wherein the first digital video signal comprising at least indirectly succeeding individual digital images is analyzed in real time, it is proposed for the locating of patterns of movement in a first digital video signal, during the occurrence of larger moving object volumes in real time, to assign at least a first location-changing foreground region in a first individual image in a predefined manner with a predefinable number of first markings, to subsequently determine the relative movement of the first markings between the first individual image and a subsequent second individual image, to subsequently associate each of the first markings with a predefinable first environment, to assign the first and/or at least one second location-changing foreground region in a predefined manner with a predefinable number of second markings, to remove the second markings disposed inside intersecting regions of a predefinable number of intersec