A method may include transmitting a video stream of a live scene over a network at a real-time transmission speed and detecting an event associated with the video stream being transmitted. The method may include transmitting the video stream over the network at a speed lower than the real-time transmission speed during the event. Transmitting the video stream at the speed lower than the real-time transmission speed may introduce a time stretch for the video stream to be played in slow motion. The method may include reducing a bitrate of the video stream after the event and transmitting the video stream with the reduced bitrate over the network after the event to compensate for the time stretch.

A security system includes a server and at least one dual-mode image capture device. The capture device includes a camera that captures images of a field of view of a monitored area. When motion is detected in the monitored area, the capture device sends the images as video to a server, and when the motion is not detected in the monitored area, the capture device sends, at intervals, at least one still image to the server. Irrespective of whether the capture device has a cloud storage subscription, the server may store the still image(s) in a database for later retrieval as evidence of an event at or near the monitored area. In certain embodiments, when the still image(s) do not significantly differ from a reference image of the monitored area, the still image(s) may be discarded and not stored in the database.

The present disclosure relates to a method performed by a background blurring system (1) for provision of a video compression stream from a video camera (2) adapted for capturing a scene. The background blurring system identifies (1001) in a first image of the scene, at a first point in time, at least a first pixel comprised in a background of the first image. The background blurring system further determines (1002) a blurred pixel value for the at least first pixel. Moreover, the background blurring system provides (1003), subsequent the first point in time, the blurred pixel value in the video compression stream. The background blurring system furthermore identifies (1004) in a second image of the scene, at a subsequent second point in time, that the at least first pixel has altered to be comprised in a foreground of the second image. Moreover, the background blurring system provides (1005) in the video compression stream, subsequent the second point in time—continuously and/or intermittently—identified non-blurred pixel values for the at least first pixel. The background blurring system further identifies (1006) in a third image of the scene, at a subsequent third point in time, that the at least first pixel has altered to be comprised in a background of the third image, wherein a value of the at least first pixel has remained unchanged from an intermediate point in time up to the third point in time. Furthermore, when the third point in time is within a predeterminable background merge time period from the intermediate point in time, the background blurring system provides (1007), subsequent the third point in time, the blurred pixel value in the video compression stream.

The disclosure also relates to a background blurring system in accordance with the foregoing, a video camera comprising such a background blurring system, and a respective corresponding computer program product and non-volatile computer readable storage medium.

A system for situational awareness monitoring within an environment, wherein the system includes one or more processing devices configured to receive an image stream including a plurality of captured images from each of a plurality of imaging devices, the plurality of imaging devices being configured to capture images of objects within the environment and at least some of the imaging devices being positioned within the environment to have at least partially overlapping fields of view, identify overlapping images in the different image streams, the overlapping images being images captured by imaging devices having overlapping fields of view, analyse the overlapping images to determine object locations within the environment, analyse changes in the object locations over time to determine object movements within the environment, compare the object movements to situational awareness rules and use results of the comparison to identify situational awareness events.

A method of an image sensor circuit includes: providing an event camera comprising at least one pixel unit; using the event camera to sense at least one current pixel value of the at least one pixel unit to detect whether at least one pixel value changes; when the at least one pixel value changes, using the event camera to trigger the digital processing circuit when the digital processing circuit is in a power saving mode and transmit information of the at least one pixel value to the digital processing circuit.

The present invention provides a learning apparatus (10) including: an acquisition unit (11) that acquires an image; a similarity computation unit (12) that computes a similarity between the acquired image, and a first image being accumulated in advance and indicating an abnormal state; a registration unit (13) that registers, as a second image indicating a normal state, the acquired image whose similarity is equal to or less than a first reference value; and a learning unit (14) that generates an estimation model for discriminating between normal and abnormal by machine learning using the first image and the second image.

Disclosed herein are methods and systems for visually identifying anomaly events, comprising an edge node configured for applying a limited resources classifier to a plurality of images captured by imaging sensor(s) deployed to monitor a certain scene relating to a certain area to classify object(s) detected in the images, applying a trained context based Machine Learning (ML) model to classification data generated by the limited resources classifier to compute an anomaly score for potential anomaly event(s) relating to the detected object(s) based on one or more contextual attributes associated with the certain scene and transmitting one or more of the images to a remote server in case the anomaly score exceeds a threshold. The remote server is configured to further apply high performance visual analysis tool(s) to visually analyze the received image(s) in order to identify the one or more potential anomaly events.

The present application describes a system and method for extending a range of an image detection and classification system that is associated with various image capture devices. The range of the image detection and classification system is extended using one or more of an optical zoom on an area of interest, a digital zoom on the area of interest and a crop operation on the area of interest.

A system for situational awareness monitoring within an environment, wherein the system includes one or more processing devices configured to receive an image stream including a plurality of captured images from each of a plurality of imaging devices, the plurality of imaging devices being configured to capture images of objects within the environment and at least some of the imaging devices being positioned within the environment to have at least partially overlapping fields of view, identify overlapping images in the different image streams, the overlapping images being images captured by imaging devices having overlapping fields of view, analyse the overlapping images to determine object locations within the environment, analyse changes in the object locations over time to determine object movements within the environment, compare the object movements to situational awareness rules and use results of the comparison to identify situational awareness events.

A system, method and apparatus for configuring a person detection sensor. The person detection sensor may limit its transmissions in accordance with a pre-configured dwell time. The person detection sensor may receive a new dwell time from a personal communication device. When the new dwell time is received, it is stored in memory and is then used to regulate the number of transmissions of the person detection sensor in accordance with the new dwell time.