186 results about "Camera network" patented technology

Some implementations of the invention provide real-time navigation data via a mobile device. Some embodiments of the invention provide portable devices that can indicate both relatively “static” information, such as map data, architectural features, casino layout information, etc., which may be updated from time to time. Accordingly, the term “static” as used herein does not necessarily mean unchanging or unchangeable. Some such embodiments provide portable devices that can simultaneously display static information and real-time video data. The video data may be provided by one or more cameras in a camera network. Information, such as offers, advertisements, etc., may be provided to a user according to the user's location.

An autonomous computer based method and system is described for personalized production of videos such as team sport videos such as basketball videos from multi-sensored data under limited display resolution. Embodiments of the present invention relate to the selection of a view to display from among the multiple video streams captured by the camera network. Technical solutions are provided to provide perceptual comfort as well as an efficient integration of contextual information, which is implemented, for example, by smoothing generated viewpoint/camera sequences to alleviate flickering visual artefacts and discontinuous story-telling artefacts. A design and implementation of the viewpoint selection process is disclosed that has been verified by experiments, which shows that the method and system of the present invention efficiently distribute the processing load across cameras, and effectively selects viewpoints that cover the team action at hand while avoiding major perceptual artefacts.

A camera detects devices, such as other cameras, smart devices, and access points, with which the camera may communicate. The camera may alternate between operating as a wireless station and a wireless access point. The camera may connect to and receive credentials from a device for another device to which it is not connected. In one embodiment, the camera is configured to operate as a wireless access point, and is configured to receive credentials from a smart device operating as a wireless station. The camera may then transfer the credentials to additional cameras, each configured to operate as wireless stations. The camera and additional cameras may connect to a smart device directly or indirectly (for instance, through an access point), and the smart device may change the camera mode of the cameras. The initial modes of the cameras may be preserved and restored by the smart device upon disconnection.

A system and method for providing an adaptive camera network is provided. The invention discloses a camera network comprising a plurality of cameras, wherein each camera in the network comprises a plurality of applications that include a first application for performing a primary function and a second application for changing the primary function and a trigger based on an event, the trigger activating the second application in at least a portion of the plurality of cameras.

A system that incorporates teachings of the present disclosure may include, for example, receiving location information associated with a mobile communication device, determining a first location of the mobile communication device based on the location information, selecting a first camera from a group of cameras based on the determined first location, receiving at least one first image from the selected first camera that captures at least a portion of the first location, performing image recognition on at least one second image to identify a user associated with the mobile communication device, selecting another camera from the group of cameras based on a determined position of the identified user, and receiving at least another image from the selected other camera. Other embodiments are disclosed.

Image-matching tracks the movements of the objects from initial camera scenes to ending camera scenes in non-overlapping cameras. Paths are defined through scenes for pairings of initial and ending cameras by different respective scene entry and exit points. For each of said camera pairings a combination path having a highest total number of tracked movements relative to all other combinations of one path through the initial and ending camera scene is chosen, and the scene exit point of the selected path through the initial camera and the scene entry point of the selected path into the ending camera define a path connection of the initial camera scene to the ending camera scene.

The present disclosure relates to a sensor network including a plurality of nodes, each node having a directional sensor, a communication module, and a processor configured to receive local measurements of a calibration object from the directional sensor, receive additional measurements of the calibration object from neighboring nodes via the communication module, estimate an initial set of calibration parameters in response to the local and additional measurements, receive additional sets of calibration parameters from neighboring nodes via the communication module, and recursively estimate an updated set of calibration parameters in response to the additional sets of calibration parameters. Additional systems and methods for calibrating a large network of camera nodes are disclosed.

A method for automatically estimating the spatial positions between cameras in a camera network utilizes unique identifying signals, such as RFID signals, transmitting between nearby cameras to estimate the relative distances or positions between cameras from received signal strength (RSS), time of arrival (TOA), or time difference of arrival (TDOA) measurements to thereby determine the neighboring relationship among the cameras. A discover-locate process can be used to discover, from the estimated relative distances, unknown cameras in the vicinity of at least three cameras at known locations. Absolute locations of the discovered unknown cameras can then be calculated using a geometric calculation. The discover-locate process can be cascaded throughout the network to discover and locate all unknown cameras automatically using previously discovered and located cameras. Such methods can be implemented in systems having cameras with transceivers integrated therein and a controller operably linked to the cameras.

The invention provides a method for calculating traces of a vehicle in a multi-camera scene, comprising the steps of identifying vehicle monitoring videos collected by the cameras by use of a convolutional neural network to acquire identification information of a target vehicle, wherein the identification information includes a model, a brand, an actual running direction and an inclination angle of the target vehicle, and a position of the target vehicle is labeled by a matrix frame; tracking the target vehicle according to an overlapping area of the matrix frame and a tracker storage region to acquire motion information of the target vehicle, wherein the motion information includes an occurrence position, a departure position, a running direction and a running trace of the target vehicle; calibrating a topological structure of a camera network according to the motion information of the target vehicle, mounting positions of the cameras and a path connected relation; and associating the traces of the target vehicle in the camera network through Bayesian estimation. The method improves the accuracy rate of calculation of the traces of the vehicle in the multi-camera scene.

A peer-to-peer camera sensor network is defined using intelligent cameras that employ iSCSI initiators and iSCSI targets to read and write data and to send control commands directly across the network as iSCSI blocks. Because data transfer rides on top of the internet protocol, cameras can be directly attached to the internet without requiring attachment to a host computer. Image data represented as video frames are mapped onto iSCSI data blocks and are shared among the cameras directly to form an efficient virtual block level video image frame storage that can be directly manipulated by multiple cameras without data replications. Transport of multiple streams, associated with scalable or multilevel MPEG encoding scheme for instance, are automatically sequenced and realigned as the iSCSI data blocks are time-aligned.

The application discloses a video-analysis-based commercial intelligent operation decision-making support system and method, so that the benefits of operation decision making are increased. The system is composed of a camera network, a video identification module, and a data analysis module. The video identification module carries out video identification to obtain customer attribute data like genders and ages and the like; the data analysis module receives the customer attribute data from the video identification module and generates data expressing a relation between customer and marketing by combining shopping mall marketing management data. The marketing management data include a commodity settlement list. The customer attribute data include information of the number, genders, and ages. The data expressing a relation between customer and marketing include a customer number proportion of customers purchasing, browsing, or exchanging commodities with specific types, a gender proportion, and age distribution information and the like. According to the invention, the provided method consists of a field image shooting step, a video identification step, and a data analysis step. The customer behavior is analyzed in real time; the marketing activities can be arranged rapidly and reasonably and the marketing strategy can be adjusted; and the service quality can be enhanced.

The invention discloses a visual saliency and multiple characteristics-based pedestrian re-recognition method. The method comprises: quickly detecting a pedestrian target in a video; partitioning a pedestrian region by virtue of a saliency detection algorithm and extracting a spatial histogram of a target region; partitioning a human body into three parts: a head, a trunk and legs by virtue of a SCA method and extracting color, position, shape and texture characteristics of the trunk and the legs; calculating the similarity of the spatial histogram by adopting an improved JSD similarity measurement criterion for primary recognition of the target; and calculating the similarity of a regional characteristic set by virtue of a Gaussian function to obtain a final recognition result. The method provided by the invention can be used for realizing long-term tracking and monitoring of special pedestrians in a multi-camera network in different background environments and under different camera settings. The method is used for realizing intelligentized processing of monitoring vidoes and immediately responding to unusual conditions in the videos, and moreover, a lot of manpower and material resources can be saved.

The invention discloses a pedestrian identification method of a camera network based on multi-level depth feature fusion. A new network model is learnt on a pedestrian database by migrating parameters of a pre-training network to the pedestrian database, a plurality of multi-level depth features are extracted using the new network model, and then a Softmax classifier in the last layer of a convolutional neural network is replaced by an SVM classifier to achieve the purpose of making full use of the multi-layer depth features. Furthermore, the multi-level depth features are used for constructing a plurality of groups of SVM classifiers of binary classification and decision values of these binary classifiers are linearly weighted to obtain final classification results. The invention can effectively improve the accuracy of recognizing a pedestrian target by way of multi-level feature fusion in a decision-making layer of the SVM classifier.

The invention discloses a multimode information system based on sensing information and target tracking. The multimode information system is characterized by comprising a visual information collecting module, a non-visual information collecting module, and an information matching module. The visual information collecting module is established on a distributed multi-camera network and comprises a camera system node and an inspection point camera group. The non-visual information collecting module comprises a sensor information collecting part. The information matching module comprises a cross-camera perform re-identification result module and a multimode data matching module. The multimode information system is wide in application range, and capable of reducing background image identification processing pressure and providing long-acting historical data, and can be widely used in the fields of person tracking, behavior excavation, and public security.

A method that utilizes high-definition street level images provided by a network of stationary traffic cameras to identify potential hazards or concerns located beyond the range of vehicle mounted devices, and to provide an advanced warning or to take some other remedial action in response thereto. In one embodiment, the method uses multiple items taken from the street level images to corroborate a potential concern before saving that concern to a concern profile where the concern is linked or otherwise associated with a particular geographic zone. By taking remedial actions well in advance of a potential concern, the method provides more opportunity to adjust or otherwise address the potential concern, which can be particularly advantageous when the host vehicle is being operated in an automated driving mode.

A method for automatic spatial calibration of a network of cameras along a road includes, processing a frame that is obtained from each camera of the network to automatically identify an image of a pattern of road markings that have a known spatial relationship to one another. The identified images are used to calculate a position of each camera relative to the pattern of road markings that is imaged by that camera. Geographical information is applied to calculate an absolute position of a field of view of each camera. A global optimization is applied to adjust the absolute position of the field of view of each camera of the camera network relative to an absolute position of the fields of view of other cameras of the camera network

The invention discloses a consistency constraint feature learning-based pedestrian re-identification method. The method includes the following steps that: S1, pedestrian images are obtained through a camera network, and a training set is marked, parameters are set, and a convolutional neural network is initialized; S2, a picture subset is sampled from a database, the convolutional neural network is adopted to extract feature information, and the similarity matrix of all pedestrians is calculated according to the feature information; S3, the optimal match of the relation matrix of all the pedestrians is solved according to a preset objective function and a preset gradient search method; S4, a gradient backward result is solved according to the difference of the optimal match of the relation matrix of all the pedestrians and a relation matrix of the all pedestrians which is obtained according to actual conditions, and the convolutional neural network is trained according to the gradient backward result; and S5, the step S2 to the step S4 are repeated until the requirements of a user are satisfied. The consistency constraint feature learning-based pedestrian re-recognition method of the invention is applicable to a condition in which matching is performed under a large-scale camera network and eliminating conflicting matching errors.

A system and method for automatically determining the camera field of view in a camera network. The system has a plurality of spatially separated cameras and direction sensors, carried on respective cameras, configured to measure the angle directions of the field of views of the cameras. Elevation sensors are operably coupled to respective cameras to measure the elevation angles of thereof. A controller is configured to process direction and elevation measurement signals transmitted from the cameras to automatically determine the cameras' fields of views. One or more cameras having a field of view containing or nearby an event of interest can be selected from the determined field of views and indicated to a user via a graphical user interface. Selected cameras which are rotatably mounted can be rotated if need be to automatically bring the event of interest into the field of views of the selected cameras.

The invention provides a trans-camera target tracking method and a trans-camera target tracking device on the basis of dynamic path trees. The trans-camera target tracking method includes carrying out single-camera target tracking on specified targets in the visual angle range of each optional camera in each preliminarily constructed camera network until the specified targets leave the visual angle range of the current camera; acquiring currently required-to-be-searched camera lists according to target search region switching algorithms; detecting objects for video data of all cameras in the camera lists and screening suspicious objects by the aid of visual similarity matching algorithms; processing the suspicious objects according to dynamic path tree models and inferring movement tracks of the specified targets. The similarity of the visual appearance of the suspicious objects and the visual appearance of the specified targets is higher than preset thresholds. The trans-camera target tracking method and the trans-camera target tracking device have the advantages that accurate trans-camera target tracking can be carried out under the condition of the possibly minimum computational resource expenditure, and paths can be accurately predicted.

The invention discloses a method and a device for measuring the attitude and displacement deformation of a large structural body through network image pickup of cameras, wherein a camera network is well laid out according to the measuring requirements and the actual conditions. The device comprises a computing control center and network nodes, wherein the network nodes generally consist of attribute blocks and cameras (groups), which are fixedly connected with each other. The method comprises: demarcating camera parameters of measuring base stations at all levels before test, and demarcating relative position attitude parameters between the attribute blocks and the cameras, position attitude parameters of the attribute blocks or the cameras, which are fixedly connected with a measuring reference, relative to the reference coordinate system, and the like; triggering the cameras in real time for image pickup by the control center; and sending data to the control center, resolving the three-dimensional position attitude of an object to be measured relative to the measuring reference through the three-dimensional position attitude between various adjacent measuring base stations, and outputting the measuring result so as to dynamically and precisely measure the spatial structure, the spatial position, the attitude and the deformation of the large structural body in real time.

The invention discloses a target tracking method based on edge computing, which mainly shoots a target through an unmanned aerial vehicle, assists in shooting the target through a ground camera network when the unmanned aerial vehicle cannot work, and an edge node computing cluster formed by ground terminal equipment is responsible for processing and analyzing video data streams. When the unmannedaerial vehicle cannot normally track, the ground camera network and the edge nodes are selectively requested to be combined to participate in a tracking task, so that the robustness of unmanned aerial vehicle tracking can be improved, and even if a final target is completely lost, the target can be efficiently found again to continue tracking due to near field communication between the edge nodesand the ground camera network; the container technology is used for isolating platform differences, so that the problem of incompatibility of video analysis and processing applications on different platforms can be solved; the ground-air cooperative mode is reasonably selected according to the current tracked state of the target, so that the energy consumption of the whole network can be reduced,and the response time of the network can be prolonged.