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

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