Abstract: Systems and methods are provided for traversing virtual spaces. The system receives image data of respective views of an environment simultaneously captured by a plurality of cameras. The system estimates a location of a subject based on the image data. The system selects a group of one or more cameras of the plurality of cameras based on the estimated location of the subject. The system generates a bitstream based on additional image data of respective views of the environment captured by the group of one or more cameras.

Abstract: A control apparatus includes a processor that controls a surveillance camera. The processor enables switching between a first surveillance mode in which the surveillance camera is caused to perform imaging to acquire a first captured image and an imaging range is changed according to a given instruction, and a second surveillance mode in which the surveillance camera is caused to perform imaging to acquire a second captured image, a trained model that has been trained through machine learning is used to detect an object that appears in the second captured image, and the imaging range is changed according to a detection result, and outputs the first captured image acquired in the first surveillance mode as a teacher image for the machine learning.

Abstract: A method for prioritization of video processing resources in a video processing and/or management system is provided. The method includes receiving video streams from multiple cameras and automatically ranking each camera as either a higher-priority or lower-priority camera. For higher-priority cameras, video content analysis is performed (as background processes), and the result is stored in storage. For lower-priority cameras, no such analysis is performed. The ranking is based on at least one of a historical usability, a historical cost, and a historical usability-to-cost ratio of a camera. A corresponding camera monitoring system, video processing and/or management system, computer program and computer program product are also provided.

Abstract: A moving object detection system detecting moving objects based on a captured image comprising: cameras capturing images to generate image data; image recognition circuitries each performing image recognition on the image data generated by each camera to detect a moving object in a captured image indicated by the image data; and an information management apparatus managing image data supplied from each camera to a corresponding image recognition circuitry. The information management apparatus includes a communication interface performing data communication with the cameras and the image recognition circuitries, and a controller managing congestion information on the number of moving objects localized within a range of a captured image indicated by image data for each camera, and controlling the communication interface to supply the image data generated by the cameras to each image recognition circuitry, distributing the image data according to the congestion information.

Abstract: In a system including a processor and memory, the memory includes instructions that, when executed by the processor, cause the processor to control the system to perform receiving a video stream capturing objects; identifying, based on the received video stream, object areas corresponding to the objects, respectively; tracking the object areas in the received video stream; generating, based on the tracking of the object areas, visual data sets at a plurality of times, wherein each visual data set is generated at a different time and includes visual data representing each object area; determining a priority of each visual data in each visual data set; selecting, based on the determined priority of each visual data, a group of the visual data to be transmitted to a remote system; and transmitting, to the remote system, the selected group of the visual data.

Abstract: There is provided a method for determining a location of a shopping container within a store, the method includes (i) acquiring by a first side camera associated with the shopping container, a side first side image of first content located to a first side of the shopping container; (ii) determining a pose of the first side camera, based on the first side image and by a machine learning process trained using a structure from motion model of the store, wherein the structure from motion model is generated from untagged side images acquired by side cameras of shopping containers that moved within the store during an image acquisition process, wherein the untagged side images are associated with side cameras pose information learnt during the generation of the structure from motion model; and (iii) determining the location of the shopping container based on the pose of the first camera.

Abstract: An information processing apparatus (100) includes: a person detecting unit (102) that executes processing of detecting a person and a location with respect to each of a first image generated in a first camera (5a) and a second image generated in a second camera (5b); a coordinate generating unit (104) that generates coordinates of the location of the detected person; and a determination unit (106) that determines, by using a first location generated from the first image and a second location generated from the second image, whether the person detected from the first image and the person detected from the second image are the same person.

Abstract: Implementations of the present specification disclose a vehicle damage assessment method, apparatus, and device. The method includes: collecting a first video image including an identification of a target vehicle under damage assessment; in response to that the first video image meets the determined requirement for capturing an identification, collecting a second video image including a damage to the target vehicle; and assessing the damage to the target vehicle based on the first video image and the second video image in response to that the second video image meets the determined requirement for capturing a vehicle damage feature.

Abstract: A method for detecting lost image information via a lighting device and an optical sensor. The lighting device and the optical sensor are controlled so as to be chronologically aligned with each other. A visible spacing region in an observation region of the optical sensor is determined from the chronological alignment of the control of the lighting device and the optical sensor. A recording of the observation region with the optical sensor is generated via the aligned control. Image information is identified in the recording in regions outside of the spacing region visible in the image, so as to make the identified image information accessible.

Abstract: An information processing apparatus includes a computer executing instructions which, when executed by the computer, cause the information processing apparatus to: detect, from among a plurality of image capturing apparatuses, a first image capturing apparatus a state of which is changed from (a) automatic image capturing in which the first image capturing apparatus automatically tracks and captures a first object to (b) manual image capturing in which the first image capturing apparatus captures a second object different from the first object based on an instruction from a user, and cause a second image capturing apparatus from among the image capturing apparatuses other than the first image capturing apparatus to perform the automatic image capturing in which the second image capturing apparatus automatically tracks and captures the first object, in a case where the first image capturing apparatus is detected.

Abstract: The present disclosure provides a system for real-time health monitoring of one or more users. The system includes a computing device associated with a facility, a plurality of cameras, an AI edge device, and a communication network. Moreover, the system includes a server, a database, and a notification interface. The AI edge device monitors the health of one or more users in real-time. The AI edge device monitors the health of the one or more users by processing a video feed captured by the plurality of cameras. Further, the AI edge device notifies the health-related issues in real-time using the notification interface to a first set of users.

Abstract: A video transmission apparatus (10) includes: a specified-position information acquisition unit (110) that acquires specified-position information indicating a specified position in an area to be surveilled including each imaging region corresponding to each of a plurality of imaging apparatuses; and a video transmission control unit (120) that selects at least one imaging apparatus from among the plurality of imaging apparatuses using imaging position information indicating a position or an imaging range of each of the plurality of imaging apparatuses and the specified-position information and transmits a video captured by the selected imaging apparatus to a video sharing apparatus that is to share the video.

Abstract: A gate opening method includes: a step (S8) of transmitting a gate opening signal for making a request for opening of a gate, which is configured to restrict entry into a predetermined space, to the gate by using an IP address unique to a mobile terminal associated with a user; a step (S9) of determining whether or not to allow the user to enter the predetermined space based on a ticket management table and the IP address of the mobile terminal in response to reception of the gate opening signal; and a step (S11) of controlling the gate so that the user is able to enter the predetermined space in response to a determination that the user is allowed to enter the predetermined space. The ticket management table includes a plurality of pieces of network address information indicating a plurality of IP addresses unique to a plurality of mobile terminals.

Abstract: Disclosed are computer systems and computer-implemented methods for training a machine-learning method for determining pre-determined points in an image. The method includes training a first machine-learning method based on training data indicating whether or not a pre-determined point is present in a cell of the image; training a second machine-learning method based on a position of pre-determined points in the cell and based on training of the first machine-learning method; and training a third machine-learning method based on a modified version of the image and based on training of the second machine-learning method. Also disclosed are methods and computer systems implementing the trained machine-learning method.

Abstract: The information processing device 4 includes an acquisition unit 40A, a structural feature point extraction unit 41A, a common coordinate system transformation unit 42A, and a structural feature point integration unit 43A. The acquisition unit 40A is configured to acquire captured images Im generated by plural image acquisition devices 5 and positional relation information Ip indicative of a positional relation among the plural image acquisition device 5. The structural feature point extraction unit 41A is configured to extract, from each of the captured images Im, intra-image coordinate values Pi for structural feature points, which are structural feature points of a target structure of observation through a display device, the display device displaying a virtual object superimposed on a view.

Abstract: An information processing apparatus (100) includes: a person detecting unit (102) that executes processing of detecting a person and a location with respect to each of a first image generated in a first camera (5a) and a second image generated in a second camera (5b); a coordinate generating unit (104) that generates coordinates of the location of the detected person; and a determination unit (106) that determines, by using a first location generated from the first image and a second location generated from the second image, whether the person detected from the first image and the person detected from the second image are the same person.

Abstract: An optical gas imaging camera, methods of operating the optical gas imaging camera in a learning mode and in an operating mode, and a compliance auditing system are disclosed, where the optical gas imaging camera has on-board hardware, devices, and software used for generating images of fugitive emissions of a target object along with generating a multitude of other information associated with the time of generating images of the target object, which is all combined into compliance data that can be stored on-board the optical gas imaging camera or transmitted to a central computer in the auditing system. The compliance data is stored and can be retrieved to add additional information such as repair of a fugitive emission or for compliance auditing purposes.

Abstract: A method for scenario triggering and interaction is disclosed. One or more camera devices (110) are used to monitor a target object (142) in a scene (140). A target object vector (143a) is generated from captured images of each camera device (110) to represent a line of sight from the camera device (110) to the target object (142). A target object position (129) in the scene (140) is then determined from an intersection of the target object vectors (143a). The target object position (129) is compared with a task triggering region (144) in the scene (140) to trigger a pre-determined task when the target object (142) is within or around the task triggering region (144). For example, the target object (142) may be a human user and augmented reality contents are presented to the human user when the human user is detected to be within or around the task triggering region (144).

Abstract: A security system can use video analytics and/or other input parameters to identify a theft event. Optionally, the security system can take remedial action in response. For example, the security system can use video analytics to determine that a person has reached into a shelf multiple times at a rate above a threshold, which can indicate that a thief is quickly removing items from the shelf. The security system can also use video analytics to determine that a person has reached into a shelf via a sweeping action, which can indicate that a thief is gathering and removing a large quantity of items from the shelf in one motion. In response, the security system can alert security personnel, cause a speaker to output an audible message in the target area, flag portions of the video relating to the theft event, activate or ready other sensors or systems, and/or the like.

Abstract: Disclosed is a multi-modal convolutional neural network (CNN) for fusing image information from a frame based camera, such as, a near infra-red (NIR) camera and an event camera for analysing facial characteristics in order to produce classifications such as head pose or eye gaze. The neural network processes image frames acquired from each camera through a plurality of convolutional layers to provide a respective set of one or more intermediate images. The network fuses at least one corresponding pair of intermediate images generated from each of image frames through an array of fusing cells. Each fusing cell is connected to at least a respective element of each intermediate image and is trained to weight each element from each intermediate image to provide the fused output. The neural network further comprises at least one task network configured to generate one or more task outputs for the region of interest.

Abstract: The disclosed system and method relate to automatically detecting empty spaces on retail store shelves, identifying the missing product(s) and causing the space to be replenished or restocked. For example, stores may use shelf-mounted imaging devices to capture images of shelves across the aisle from the imaging devices. The images captured by the imaging devices may be pre-processed to de-warp, de-skew images and stitch together multiple images in order to retrieve an image that captures a full width of a shelf. The pre-processed images can then be used to detect products on the shelf, identify the detected products. For example, the captured image may be compared against a reference background image using a background modeling algorithm to identify empty spaces and mis-shelved items within the shelf.

Abstract: A system and method for synchronizing two-dimensional (“2D”) camera data for object recognition. An object recognition kiosk includes a plurality of 2D cameras and a stage for placement of one or more items. The plurality of 2D cameras, one of which acts as a reference camera, capture images of items on the stage from multiple angles. The system synchronizes image data from each camera in order to accurately identify items present on the stage. In situations where items in camera images fail to be linked to items identified in the reference camera perspective, the system maintains a fallback hierarchy of camera pairs to utilize to link items found in different camera images. The use of different camera pairs to link item detections continues down the fallback hierarchy until either all the item detections are linked or the last camera pair in the fallback hierarchy is reached.

Abstract: Media, methods, and systems are disclosed for processing composite streaming video in connection with a centralized service. A request is received to initiate mixing a composite video live stream having a constituent stream quantity, one or more streaming video assets, and one or more media providers. If needed, one or more subsequent media servers are provisioned. The one or more constituent video streams and the one or more streaming video assets are received at a mixing server. The composite video live stream is rendered based thereon. The rendered composite video live stream is encoded into a standardized encoded video stream, which may be stored in one or more shared memories. The standardized encoded video stream is encoded into a provider-specific encoded media stream, which is then transmitted to a corresponding media provider.

Abstract: A method is described. The method can include establishing a block chain network of multiple imaging devices. The block chain network can be established such that, at least a first imaging device of the multiple imaging devices can be validated by at least a second imaging device of the multiple imaging devices. Further, the method can include generating a block message by the first imaging device. The block message can be generated by using a secured hash function and based on a set of parameters that can be associated with the first imaging device. The set of parameters can include, a first imaging device identifier, a network fluctuation parameter, and at least one inertial parameter. Furthermore, the method can include sending by the first imaging device the block message to the second imaging device. In this regard, the block message can facilitate validation of the first imaging device.

Abstract: Use of multiple sensors to determine whether motion of an object is occurring in an area is described. In one aspect, an infrared (IR) sensor can be supplemented with a radar sensor to determine whether the determined motion of an object is not a false positive.

Abstract: Detection models are distributed as needed to a select subset of image capture devices, based on geographic location and time of the image capture devices. Each image capture device which receives a model processes image data according to the model, and provides a notification of any detected instances of an event the model is trained to detect. Distribution and detection can be based on historical data or live data.

Abstract: An information processing system includes an image-capturing apparatus and an information processing apparatus, the image-capturing apparatus including an image-capturing section that is used to obtain a captured image of a subject, and a feature detector that detects, on the basis of the captured image, feature information that is information indicating a feature of the subject, the image-capturing apparatus or the information processing apparatus including a determination section that determines whether there exists a target subject in the captured image on the basis of detected feature information that is the feature information detected by the feature detector, and on the basis of target feature information that is the feature information to be targeted, the target subject being a subject for which the detected feature information matches or is similar to the target feature information.

Abstract: Systems and methods are provided for detecting the presence of a hidden camera on a network. When video is encoded and transmitted over/across a network, the data packet carrying the video tend to exhibit certain characteristics or features specific to video traffic from a hidden camera. A machine learning model for detecting the presence of a hidden camera can be trained based on these characteristics and features. Once trained, the machine learning model can be operationalized on an access point that can analyze real-time network traffic to determine whether a hidden camera(s) is operating on the network.

Abstract: A monitor device is provided, including: a fixing seat, for fixing to a structure; a casing, with an accommodating space, upper end of the longitudinal axis of the casing rotatably connected to the fixing seat; a first optoelectronic device, disposed on the lower end of the casing; a second photoelectric device and a third photoelectric device, respectively rotatably disposed at a first position and a second position outside the casing; a transmission mechanism, disposed in the accommodating space of the casing, and the transmission mechanism including: an angle rotation mechanism, connected to the fixing seat to drive the casing to rotate in a first direction relative to the fixing seat; and a second angle rotation mechanism connected to and driving the first, the second and the third optoelectronic device to rotate synchronously in a second direction; wherein the first direction and the second direction are orthogonal to each other.

Abstract: Systems, methods, and software described herein manage the storage of video data in multiple storage locations. In one example, a system obtains video data from a video source and stores the video data in a first format in a first storage location. Additionally, the system stores the video data in a second format in a second storage location, wherein the second format comprises one or more lesser video quality characteristics than the first format. The system further identifies metadata associated with the video data and stores the metadata in the second storage location.

Abstract: A method and system for privacy-aware movement tracking includes receiving a series of images of a field of view, such as captured by a camera. The images containing movement of an unidentified person within the field of view. A body region corresponding to the person is detected within the images. A movement dataset for the unidentified person is generated based on tracking movement of the body region over the fired of view within the images is generated. A characterizing feature set is determined for the unidentified person. The set is associated within the movement dataset to form a first track entry. Anonymizing of the body region can be applied to remove identifying features while or prior to determining the characterizing feature set. A second track entry can be generated from a second series of images and match between the track entries can be determined. A method and system for privacy-aware operation and learning of a computer-implemented classification module is also contemplated.

Abstract: There is provided an image acquisition controller for acquiring an adapted image. The image acquisition controller comprises an interface towards a first image sensor, the first image sensor being arranged to acquire an image, and an interface towards a gaze tracking unit, the gaze tracking unit comprising a second image sensor for registering a gaze position on a scene associated with the image acquired by the first image sensor. The image acquisition controller is arranged to periodically receive information over the interface towards the gaze tracking unit about at least a position on the scene associated with the image provided from the first image sensor where the scene represents a larger part of an image registered by the first image sensor than intended to be acquired as the adapted image.

Abstract: A domestic appliance, which may be a cooling device, such as a refrigerator, includes: a treatment chamber that can be closed by a pivotable door; at least one camera arranged in the door for recording images from the treatment chamber when the door is open; and a door movement sensor for detecting values of at least one parameter of a door movement. The door movement sensor has at least one inertial sensor. At least one camera and the at least one inertial sensor are integrated in a camera/sensor module which is arranged in the door. The camera/sensor module has a communication device, at least for outputting image data from the camera of the camera/sensor module. The domestic appliance is configured to trigger at least one device function upon the detection of at least one trigger value.

Abstract: A method including: requesting a web resource from a plurality of devices for setting a web UI for the plurality of devices; receiving the web resource from the plurality of devices; requesting device attribute information related to a device attribute from the plurality of devices; receiving the device attribute information from the plurality of devices; checking whether a web resource corresponding to the received device attribute information is stored in a memory of the server; configuring a common setting web UI for a common device attribute and configuring an individual setting web UI for an individual device attribute based on the web resource; and mashing up the common setting web UI and the individual setting web UI and rendering the mashed common setting web UI and individual setting web UI to a single web page of the server.

Abstract: A method, computer program product, and computing system for determining whether a storage awareness service provider node of a storage system has failed. In response to determining that the storage awareness service provider node has failed, an intermediate storage awareness service may be deployed within the storage system. At least one request may be processed on the storage system via the intermediate storage awareness service.

Abstract: To accurately identify a region corresponding to a position where an object is preset in an image, an object region identification device acquires a first frame image and a second frame image that are temporally successive. The device inputs the first frame image to a model that identifies an object in an image and acquires position information, the position affecting identification of an object in the first frame image. The device acquires motion information indicating a motion of the object in the first frame image based on the first frame image and the second frame image. The device generates, based on the acquired position information and motion information, region information indicating a region in the first frame image, the region corresponding to a position where the object is present.

Abstract: A computer-implemented method includes assigning each access device of a plurality of access devices to at least one respective space of a plurality of spaces, including assigning a first access device to a first space of the plurality of spaces, based on the first access device controlling access to the first space. One or more access devices, from among the plurality of access devices, that are assigned to the first space are grouped together into a first group of devices. A boundary of the first space is generated based at least in part on access data generated by the first group of devices, where the access data describes access to the first space by way of the one or more access devices in the first group of devices.

Abstract: An electronic device comprises an image sensor, one or more processors, and memory storing instructions for receiving an event recording profile based on configuration data of the electronic device, the configuration data including a location type or a power type; receiving a plurality of images of a scene captured by the image sensor; detecting a trigger event based on one or more of the plurality of images of the scene; in response to detecting the trigger event, identifying an object of interest in one or more of the plurality of images of the scene; creating an event clip from the stored images that include the object of interest, wherein creating the event clip includes configuring a clip length based on the event recording profile; and providing the event clip for display.

Abstract: Embodiments of this application belong to the field of audio processing technologies and provide an audio processing method and an electronic device. The method is applicable to an electronic device including a camera. The electronic device records a plurality of channels of audio and video pictures corresponding to the audio in a video recording mode, and plays, during video playback, some video pictures and audio obtained after the plurality of channels of audio are combined. When a speaker in a video picture starts to speak, played audio is switched to audio corresponding to the video picture in which the speaker is located; thereby resolving a feeling of a sudden change of sound caused by switching required for obtaining audio content when the electronic device that does not support playback of a plurality of channels of audio plays a video.

Abstract: A video signal receiver includes a clock signal receiver, a frame signal generator, and a frame signal transmitter. The clock signal receiver receives a camera video signal clock sent from a video signal transmitter in a camera module and outputs the clock to the frame signal generator. The frame signal generator generates a frame signal based on the clock received by the clock signal receiver and outputs the frame signal to the frame signal transmitter. The frame signal transmitter receives input of the frame signal output from the frame signal generator and sends the frame signal to the video signal transmitter of each camera module.

Abstract: A device, method and system for installing video analytics parameters at a video analytics engine is provided. An example device determines that a classification of a first camera is one or more of similar to, and same as, a respective classification of at least one second camera. The example device retrieves, from a memory, video analytics parameters associated with the at least one second camera, the video analytics parameters stored at the memory in association with the respective classification. The example device causes installing of the video analytics parameters at a video analytics engine associated with the first camera.

Abstract: A circuit includes a first communication interface configured to receive first sensor data from a stationary sensor. The first sensor data include a result of a first sensing of a local environment of the stationary sensor performed by the stationary sensor. The circuit may further include a second communication interface configured to receive second sensor data from an unmanned aerial vehicle. The second sensor data include a result of a second sensing of at least a portion of the local environment of the stationary sensor performed by a sensor of the unmanned aerial vehicle. The circuit may further include one or a plurality of processors configured to compare the first sensor data and the second sensor data and to classify the at least one stationary sensor based on a result of the comparison.

Abstract: A live media streaming architecture facilitates live interaction among multiple participants, such as viewers and stage performers for a show. A cluster of computers is responsible for generating, transmitting, and displaying a composed live stream, such as a grid, of one group of individuals, by mixing live streams from those individuals, that will allow them to interact with a stage performer or other contributors or other individuals during an event. The architecture can support receiving the live streams at a physical location of an event or other locations for centralized redistribution.

Abstract: Disclosed is a multi-modal convolutional neural network (CNN) for fusing image information from a frame based camera, such as, a near infra-red (NIR) camera and an event camera for analysing facial characteristics in order to produce classifications such as head pose or eye gaze. The neural network processes image frames acquired from each camera through a plurality of convolutional layers to provide a respective set of one or more intermediate images. The network fuses at least one corresponding pair of intermediate images generated from each of image frames through an array of fusing cells. Each fusing cell is connected to at least a respective element of each intermediate image and is trained to weight each element from each intermediate image to provide the fused output. The neural network further comprises at least one task network configured to generate one or more task outputs for the region of interest.

Abstract: Systems and methods provide for an automated system for analyzing damage and processing claims associated with an insured item, such as a vehicle. An enhanced claims processing server may analyze damage associated with the insured item using cameras and lasers for determining the extent and severity of the damage. To aid in this determination, the server may also interface with various internal and external databases storing reference images of undamaged items and cost estimate information for repairing previously analyzed damages of similar items. Further still, the server may generate a payment for compensating a claimant for repair of the insured item.

Abstract: A camera module includes a first lens module defining a first optical axis; a second lens module defining a second optical axis; and a first optical path changing unit disposed between the first lens module and the second lens module and configured to selectively reflect or refract light incident along a third optical axis intersecting the first and second optical axes to the first lens module or the second module.

Abstract: Systems and methods for redundant storage among networked video cameras are described. Video data for a group of video cameras in a backup cluster determined from a network topology is received by a peer video camera. A backup order of the backup cluster is received. The peer video camera sends its source video data for to a backup storage device located at a target peer video camera of the group of video cameras according to the backup order.

Abstract: A pipeline inspection device including a housing, an antenna, an imaging device having one or more lenses, two diaphragms extending from the housing and distal to one another along the length of the housing, the two diaphragms sharing a longitudinal axis with the housing, a processor, a storage device in communication with the processor, and a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is operable to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels. Also disclosed herein are systems and methods for using the same.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for using a drone to monitor a community. The drone may include a processor and a storage device storing instructions that, when executed by the processor, cause the one or more processors to perform operations. The operations may include receiving an instruction to deploy based on a determination, by a community monitoring system that an event was detected at a property of the community, navigating towards the property along an initial navigation path, obtaining local monitoring system data from a local monitoring system of a property of the community, generating based on the local monitoring system data a navigational model that identifies a location of each of one or more surveillance objectives, determining an adjusted navigation path to a location of a surveillance objective of the one or more surveillance objectives, and navigating along the adjusted navigation path.

Abstract: Described herein are systems and methods for locating an object detected in a video. The system detects a bounding box at least partially around an object in a first frame at a first time in the video and a second frame in the video corresponding to a second time. The system determines whether there is no motion within the bounding box of the second frame. The system compares edge information, or color information, or intensity information associated with one or more pixels in the first frame, to edge information, or color information, or intensity information associated with one or more pixels within the bounding box. The system generates a score based on the comparison. The system further determines based on the score if the object is present in the second frame. The system also determines an estimated timeframe window of a first appearance of the object.