546 results about "Network camera" patented technology

Systems, video cameras, and methods for predictive adjustment of multi-camera surveillance video data capture are described. A plurality of networked video camera is deployed with known spatial relationships and non-overlapping fields of view among cameras. When a video event is detected from video data for one of the video cameras, a video capture update message is selectively sent to other video cameras to modify their video capture operations. For example, an object detected by one camera may trigger adjustment of video capture operating parameters for cameras in a direction of travel of the object before it has entered the field of view of those other cameras.

Method and system for the design, execution and monitoring of works, particularly in the construction field, through the modeling of projects by means of a BIM system integrated with the construction engineering necessary to configure all the systems and equipment required for construction, including automatic generation of smart contracts starting form a contract document. In a preferred embodiment of the present invention the automatic generation of a smart contract is performed by means of SysML (System Modeling Language) tool. IoT and / or Blockchain technologies are combined with conventional BIM systems for more precise monitoring and traceability of the stages in construction. The system and method according to a preferred embodiment of the present invention use the information collected by a plurality of detectors. The plurality of detectors preferably includes IoT devices. Detectors can be associated with any machinery used for the project and may include, for example, image acquisition devices such as cameras, remote cameras or web-cams fixed or mounted on drones; other possible detectors may be worn by construction staff. Detectors may also receive information from meteorological and / or seismographic services for a geographical area linked to at least one project.

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

The invention provides an intelligent identification and real-time alarm system for video monitoring. The intelligent identification and real-time alarm system comprises a video sensor, a real-time preprocessing module, a deep learning module, a risk assessment module and an early warning module, the video sensor collects video information in an environment and sends the video information to the real-time preprocessing module, and the video sensor adopts one of a network camera IPC, an analog camera AVC and an NVR video processor; the real-time preprocessing module preprocesses the video data, the preprocessing comprises clipping, size adjustment, enhancement and denoising, standard video image formation, resolution and frame rate unification and video noise reduction, and the standard video image obtained after preprocessing is sent to the deep learning module; and the deep learning module performs target identification, target detection and abnormal behavior detection on the standard video image through a deep learning model, and analyzes the video according to the output detection target and the detection abnormal result.

Systems, video cameras, and methods for predictive adjustment of multi-camera surveillance video data capture based on graph maps are described. A plurality of networked video camera is deployed and represented in a graph map based on the video camera environment, with parent nodes corresponding to video cameras and child nodes corresponding to path intersections among the video cameras. When a video event is detected from video data for one of the video cameras, a video capture update message indicating a shared child node identifier is selectively sent to other video cameras to modify their video capture operations.

The invention relates to the technical field of computer leakage prevention, in particular to a computer defense system based on the Internet of Things, which comprises an Internet of Things equipment feature acquisition module for acquiring operation data of a computer, a printer and a network camera associated with the computer, and converging instruction time sequence, memory access and peripheral interaction information. According to the method, the dynamic reference model covering the full behavior dimension of the equipment is constructed by collecting multi-source operation data of a computer, a printer and a network camera and extracting quantitative indexes such as instruction time sequence fluctuation, memory access fluctuation and peripheral interaction frequency, and a traditional static protection mode of single encryption or access control is broken through. Spatial mapping and boundary inspection are performed on real-time operation indexes based on a dynamic reference, and automatic closed loop from abnormal signal detection to risk grade evaluation is realized in combination with a data access risk scene matching mechanism, so that the misjudgment rate and delay of manual research and judgment are effectively reduced.

The invention discloses a space-based telemetering transmission system for a carrier rocket, and relates to the technical field of rocket measurement and control communication, and the system comprises a rocket-borne network camera which is used for collecting video image data of a preset monitoring point position of the rocket, compressing the video image data, and outputting the compressed video image data as telemetering data; the telemetering transmission equipment is respectively connected with the rocket-borne network camera and the ground equipment, performs telemetering framing on the telemetering data, outputs first telemetering data and outputs second telemetering data to the ground equipment; the space-based power amplifier unit equipment is connected with the telemetering transmission equipment, receives the first telemetering data, amplifies the first telemetering data and then outputs the first telemetering data through radio frequency; the space-based telemetering antenna is connected with the space-based power amplifier unit equipment, receives the first telemetering data and then transmits the first telemetering data to a relay satellite, so that the satellite sends the first telemetering data to a ground measurement and control station; and the ground equipment is used for receiving the second telemetry data and then analyzing and uploading configuration parameter information. The problem that the coverage blind area of a telemetering system mainly based on ground-based measurement and control is large in the prior art is solved.

The invention discloses an unmanned aerial vehicle real-time identification and tracking system based on a neural network, and the system carries out the identification and tracking of a target unmanned aerial vehicle through the fusion of a millimeter-wave radar and a network camera, i.e., obtains a three-dimensional target coordinate through the millimeter-wave radar, achieves the all-weather monitoring of the target unmanned aerial vehicle, and achieves the real-time identification and tracking of the target unmanned aerial vehicle through the combination of an image recognition module of the network camera. The method achieves the precise recognition and confirmation of the type and position of a target, and improves the robustness and recognition accuracy in a complex environment. A closed-loop multi-strategy holder control algorithm is adopted, a tracking control module of the server is fused with three control strategies of proportion, fuzzy and integral differential adjustment, a holder control instruction is calculated in real time according to the target state, accurate turning and continuous tracking of a camera are achieved, the stability of the target unmanned aerial vehicle kept in the center of a picture is improved, and the target unmanned aerial vehicle is more stable. The requirements of continuous, stable and closed-loop tracking of the unmanned aerial vehicle in a complex dynamic environment are met, and the system has the technical advantages of being high in adaptive capacity, high in response speed and high in recognition precision.

The invention discloses a processing method for capturing an object image in real time based on a computer vision technology, and relates to the technical field of computer vision, and the method comprises the following steps: 1, capturing an object image in real time through a USB camera and a network camera, and transmitting image data to a server side in real time through TCP / IP for processing; step 2, removing noise by using a Gaussian filter, enhancing contrast by histogram equalization, extracting image features through a hybrid architecture based on Inception V3 and ResNet-50, optimizing feature extraction through a multi-scale attention mechanism by the hybrid architecture, performing pixel-level segmentation by using a full convolutional network, dividing the image into different object areas, and performing image segmentation on the object areas; post-processing is carried out on the segmentation result; according to the method, the Gaussian filter and the histogram equalization technology are adopted, image noise is effectively removed, the contrast ratio is enhanced, image features are extracted through the Inception network, pixel-level segmentation is carried out in combination with the full convolutional network, and high-precision and high-efficiency object segmentation is achieved.

The invention discloses a wall surface self-adaptive wheel type magnetic adsorption pipeline wall-climbing robot, which relates to the technical field of intelligent machinery and comprises a vehicle body, a passive self-adaptive device, a driving device, a permanent magnet adsorption magnetic wheel group and a sensing device, through mutual cooperation of the passive self-adaptive device, the driving device, the permanent magnet adsorption magnetic wheel set and the sensing device, the problem that when the wheel type magnetic adsorption wall-climbing robot walks on an uneven working face, the rotating speeds of all wheels are not synchronous can be solved; the wheel type magnetic adsorption wall-climbing robot smoothly walks on the working face and crosses the external corner of the pipeline, the use reliability of the robot is improved, then the working efficiency of the robot is improved, the front and back network cameras can transmit image information in the pipeline in real time, and an operator can conveniently monitor and adjust the working state of the robot.

The invention discloses an early forest fire smoke automatic detection system and method, and belongs to the field of forest fire monitoring and early warning. The system comprises a YOLOv7-Smokey small-range smoke detection network, a PyTorch framework, a TensorRT technology and an NVIDIA Jetson Nano embedded platform, and is characterized in that the YOLOv7-Smokey small-range smoke detection network is connected with the TensorRT technology through the NVIDIA Jetson Nano embedded platform. The method comprises the following steps: S1, carrying out image data collection and data enhancement on early forest fire smoke through a network and public data; s2, making an early forest fire smoke detection data set; s3, a YOLOv7-Smokey backbone network is designed, and training is carried out; s4, deploying the YOLOv7-Smokey network model to an NVIDIA (Noise Visual Identity Architecture) Jetson Nano embedded platform; s5, performing reasoning acceleration processing on the YOLOv7-Smokey network by using a TensorRT technology; s6, performing real-time early forest fire smoke detection on an NVIDIA Jetson Nano platform by using a network camera; and S7, transmitting a reasoning result to a server in a video stream pushing mode. The method is used for deploying an early forest fire smoke detection system on an NVIDIA Jetson Nano embedded platform, the real-time performance of detection is ensured while the detection precision is ensured, the operation consumption is reduced, and marginalized intelligent detection is realized.

The invention discloses an intrinsic safety type intelligent AI network camera and an image analysis system thereof, and relates to the technical field of image analysis, and the scheme is characterized by comprising an image feature analysis module, a rock stratum monitoring module, a risk assessment module and a behavior safety module. According to the method, the accuracy of crack identification and behavior analysis is improved through refined image data processing logic, the width, depth and distribution mean value of a crack pixel region are meticulously calculated, and a crack distribution table is constructed, so that the system can accurately analyze the crack expansion trend and determine the expansion direction and speed, the risk identification accuracy is improved, and the risk identification efficiency is improved. The prediction reliability of future fracture behaviors, the capability of evaluating the rock stratum stability in real time and the capability of predicting the collapse risk are enhanced, so that the system can warn potential risks in advance, provide a basis for safety management, analyze miner behaviors in real time, effectively identify potential abnormities, reduce potential safety hazards and enhance the safety monitoring and management capability of an under-mine environment.

The invention relates to the technical field of security and protection monitoring, in particular to a method and system for detecting abnormal work of a network camera, and the method comprises the steps: training a basic data set to form a large model, collecting normal and abnormal pictures, carrying out the blocking processing, labeling, preprocessing, and carrying out the multi-task learning training of a CNN network structure model. SSIM is used as a loss function, simulated abnormal data is generated to enhance generalization ability, and model performance is evaluated; after video stream key frames or fragment blocks are obtained, a result R0 is recognized through the large model, reviews regularly, if abnormity occurs, the result is output, and after a certain result is accumulated, the large model is trained. The system comprises a data acquisition module, a model training module, a detection data acquisition module, a data partitioning module, an anomaly detection module, an examination module, a model optimization module and the like. According to the method, the detection efficiency and accuracy are improved, manual intervention is reduced, the problems of low video multi-channel detection performance, tedious threshold setting and low detection accuracy in the prior art are effectively solved, and the method is suitable for various security and protection monitoring scenes.

The invention relates to the technical field of agricultural pest detection, in particular to an improved YOLOv8n-based pest detection method, which comprises the following steps of: performing data acquisition by adopting an automatic pest acquisition device consisting of trap lamp equipment, a solar panel and a network camera; constructing a pest data set; a YOLOv8n model is improved, and an iMLCA framework, a CSPPC module and a FASFFHead detection head are fused in the YOLOv8n model. According to the method, efficient detection of the pests is achieved by providing a detection model based on YOLOv8n improvement, namely a Pest-YOLOv8n algorithm, the algorithm combines an iMLCA architecture, a CSPPC module and a FASFHead detection head, higher feature extraction and detection capacity is shown, and the detection precision of the model on the pests in the complex and changeable environment is effectively improved.

The invention relates to an intelligent security management system for a sewage pumping station, which relates to the technical field of intelligent security management and comprises a front-end sensing layer, a transmission layer, a platform layer and an application layer, the front-end sensing layer is composed of a high-definition network camera deployed on the enclosure wall of the pump station and the top of a process area, a face recognition terminal installed on the side wall of a doorframe of an entrance and exit and a license plate recognition unit, the camera is powered by a POE, and the access controller is connected with a biological recognition card reader through an RS485 bus. Omnibearing monitoring and accurate identity verification of a pump station area are achieved through a high-definition network camera of the front-end sensing layer and the biological recognition technology, illegal invasion is effectively prevented, the transmission layer is combined with the wireless Mesh network through the industrial Ethernet, the stability and the anti-interference capacity of data transmission are ensured, and the service life of the transmission layer is prolonged. And the smoothness of a monitoring picture can be kept in a complex electromagnetic environment, and rapid fusion of multi-source data and efficient processing of service logic are realized.

To provide a water quality determination device and a water quality determination method that are capable of detecting abnormalities with high accuracy without erroneous detection.SOLUTION: An image data storage unit 101 stores image data of a water surface photographed by a network camera. An image data processing unit 102 processes the image data and outputs the processed image data. An image data expansion unit 103 generates abnormal dummy data from the processed image data. A learning processing unit 104 performs learning using the abnormal dummy data as teacher data, and stores learned parameters in a parameter storage unit 105. An abnormal estimation unit 106 processes the processed image data using the learned parameters and outputs a segmentation map. A water quality assessment unit 107 assesses whether a water quality is normal or abnormal based on a predetermined threshold value and the segmentation map, and outputs water quality assessment result data indicating an assessment result.SELECTED DRAWING: Figure 1

The invention relates to the technical field of riding wheel bearing bush temperature monitoring and heating early warning, and discloses a rotary kiln riding wheel bearing bush temperature monitoring and heating early warning system which comprises a health temperature monitoring unit, a heating early warning unit and a health monitoring management platform. The health temperature monitoring unit comprises a distributed data acquisition module and a data transmission and control module which are electrically connected with the health temperature monitoring unit. When information of the distributed data acquisition module is transmitted to the Internet of Things gateway, the data transmission and control module plays a key role and manages the local and remote management monitoring computer modules, so that the local and remote management monitoring computer modules work cooperatively to drive the infrared network camera to monitor the heating condition of the riding wheel bearing bush, and the camera captures real-time pictures; and the data is transmitted to a gateway of the internet of things through an RJ45 network port, so that related personnel can know the heating degree in real time and take measures in time. The module improves the transmission efficiency, enhances the real-time monitoring capability, and provides guarantee for stable operation of equipment.

The invention discloses an old network camera networking system, method and equipment based on distributed control, and particularly relates to the field of distributed control. An equipment registration and discovery module scans old network cameras in a local area network through an ONVIF protocol, obtains information of the old network cameras and generates a pull flow mapping table; the proxy device is in butt joint with an open source gap distributed device registration interface, registers to a distributed system as a distributed network camera gateway identity, and submits subordinate media acquisition terminal capability based on a pull stream mapping table; the open source gap terminal discovers proxy equipment through a soft bus, sends a media data request instruction and attaches a terminal equipment certificate of the open source gap terminal; the proxy device verifies the terminal device certificate; the RTSP stream management module creates a pull thread for each old web camera and starts to pull a video stream, and the stream video processing module is in butt joint with an open-source gap distributed equipment registration interface and then returns the video stream to an open-source gap terminal through a distributed soft bus; and the open-source terminal receives the video stream to complete networking.

The instrument detection table comprises a belt conveyor, a detection chamber is fixedly installed at the top of the belt conveyor, an access hole is formed in the top of the detection chamber in a penetrating mode, an access door is hinged to one end of the top of the detection chamber, a detection mechanism is installed in the detection chamber, and the detection mechanism is connected with the detection chamber. Through the structural design of a stepping motor, a rotating rod, a fixed block and two fixed discs, when the instrument appearance detection device is used for detecting the appearance of an instrument, and a high-definition network camera on one fixed disc breaks down during working, the positions of the two fixed discs can be exchanged through the work of the stepping motor, so that the appearance of the instrument is detected. Therefore, normal appearance detection operation of the instrument can be guaranteed, and the problem that the reliability of equipment and the overall detection working efficiency of the instrument are greatly reduced due to the fact that the whole equipment needs to stop working for maintenance when a traditional instrument detection device is generally provided with only one set of camera shooting assembly and the assembly breaks down is solved.

A secure transmission method includes an Internet Protocol (IP) camera (IPC) that encrypts a to-be-transmitted video stream based on a pre-obtained first stream key to obtain an encrypted video stream. The IPC encrypts the first stream key based on a pre-obtained first wrapping key to obtain a first encrypted stream key. The IPC sends a data stream to a cloud platform, where the data stream includes the encrypted video stream and the first encrypted stream key. The cloud platform stores the data stream. A user equipment receives the to-be-processed data stream from the cloud platform. The user equipment decrypts the first encrypted stream key based on a pre-obtained first wrapping key to obtain the first stream key. The user equipment decrypts the encrypted video stream based on the first stream key to obtain a to-be-played video stream.

The invention discloses an active and passive combined video monitoring system fault positioning method, which comprises the following steps of: S1, acquiring a real-time monitoring video stream, and acquiring video frame data, a video frame statistical index and video stream session information from the real-time monitoring video stream; s2, analyzing the video frame data to obtain a video fault index; s3, detecting a video server and a network path condition to obtain a network degradation condition of the network node; and S4, carrying out association analysis by taking the video fault index, the network path and the network degradation condition as data sources, and positioning the fault. According to the invention, from user perception, accurate positioning of video faults is realized, network camera faults, network transmission node faults and server faults can be positioned, an accurate monitoring means is provided for rapid repair of the video faults, and the video fault inspection and repair cost is greatly reduced.

The invention relates to the technical field of monitoring, in particular to a multi-network camera processing method based on Qt Creator. According to the invention, video capture, processing and display are processed in a multi-thread mode, and the video stream of each network camera is managed by an independent thread, so that the response speed and stability of the system are effectively improved. According to the invention, a user can select different video layout modes as required, including a single-picture mode, a single-picture full-screen mode, a one-main multi-secondary-picture mode and a multi-picture tiling mode. And meanwhile, the user can reasonably arrange video pictures according to monitoring requirements, so that the readability of information and the convenience of operation are improved. According to the invention, the RSTP / HTTP protocol is adopted, so that the rapid loading of the camera picture can be realized, the transmission delay of the video stream is reduced, the response speed of the system is effectively improved, and the operation experience is improved.

The invention discloses a crown block safety monitoring system based on YOLO V5 and Wincc, belongs to the field of monitoring systems, and aims to solve the problems that a crown block monitoring system mainly depends on manual judgment and is easily influenced by experience and judgment of an operator, and the operation risk is increased. The system comprises a data acquisition module, an image vision module, a hoisting state judgment module and an abnormity alarm module, the data acquisition module is used for acquiring variable point locations of the crown block PLC in real time; the method comprises the following steps: reading a variable point location in a PLC (Programmable Logic Controller) through KepServerEX software; the image vision module is used for recording a steel ladle trunnion position working image in real time from the front angle through n high-definition network cameras mounted on the two sides of a crown block working area and transmitting the steel ladle trunnion position working image to the hoisting state judgment module; the hoisting state judgment module is used for receiving the steel ladle trunnion position working image transmitted from the image vision module; and performing visual image identification on the steel ladle trunnion position working image based on a YOLO V5 algorithm, and identifying whether a lifting lug of the steel ladle and a lifting hook of the crown block are in safe positions or not.

The invention provides a cross-platform brain-computer interface normal form system suitable for natural state monitoring, and the system comprises a cross-platform development module which achieves the operation of a set of codes on a plurality of platforms through an owner-drawing engine; the task integration and expansion module is used for integrating various brain-computer interface normal form tasks; establishing a local material library management mechanism, and performing association configuration on task types and stimulation materials through a data configuration file; according to the multimedia precise synchronous control module, an asynchronous programming mechanism and a timer function of a Flutter are adopted, when audio and video materials are played or ended, a hardware circuit or streaming data of an electroencephalogram collecting device, a physiological multi-guide instrument or a network camera is labeled, alignment of multi-device and multi-modal data is achieved, and the multi-device and multi-modal data are integrated. Generating a task log containing a timestamp mark; and the natural stimulation task module is used for incorporating a task for simulating a natural scene into a brain-computer interface normal form. And a brain-computer interface system which gives consideration to cross-platform consistency and flexible configuration of natural scene tasks is provided.

The invention relates to the technical field of under-mine network image definition enhancement, in particular to a network camera image definition enhancement method and system based on deep learning. The method comprises the following steps: obtaining a network camera image of a mine underground operation area, carrying out quality label classification processing on the image, and constructing a training sample containing multiple types of image features; analyzing the difference characteristics of the training samples, and determining the degradation characteristics of the under-mine image; the method comprises the following steps: analyzing the structural loss damage degree of a key area of an image under a repaired mine based on quality reduction characteristics, generating the structural definition of the image, carrying out deep learning model training and parameter optimization based on the structural definition, determining image enhancement parameter configuration, and embedding the image enhancement parameter configuration into an image processing module of a mine network camera system. Real-time enhancement processing on the acquired image is realized; according to the invention, the definition of the network camera image is enhanced, so that the equipment under the mine runs more efficiently.

The invention discloses a video processing method and device based on OpenHarmony, electronic equipment and a storage medium, and relates to a video security encryption technology, the method comprises the following steps: networking a network camera according to a star flash technology, determining a management node and a terminal node, the network camera carrying a SwanLink OS developed based on OpenHarmony; the management node is used for determining a time mapping relation according to the time information, and the time mapping relation represents a mapping relation between the management node or the terminal node and daily time; sending the generated initial vector to a terminal node; the terminal node is used for generating an encrypted ciphertext according to the initial vector and local information; in response to the shooting instruction, determining a target node according to the triggering time of the shooting instruction and the time mapping relationship; and the target node encrypts a shot image plaintext based on a symmetric encryption algorithm according to the encrypted ciphertext and the verification information to obtain an image ciphertext. The security and encryption efficiency of the network camera can be improved.

The invention discloses an air conditioner purification unit filter assembly dust accumulation amount recognition and replacement early warning system and method, and relates to the technical field of intelligent recognition and early warning. The system comprises an image acquisition module which at least comprises a 4K automatic focusing high-definition network camera, a high-resolution black and white camera and a CCD pinhole type high-definition network camera, and further comprises an image data transmission module, a data storage processing platform, a dust accumulation amount intelligent identification system, a dust accumulation data transmission module and an early warning control module. The early warning control module at least comprises three prompts in the early warning process, and the three prompts are short message notifications including display screen display alarm and indicator lamp alarm. On the basis of machine learning, the efficient, rapid and low-cost intelligent recognition method for the dust accumulation amount of the air conditioner filter assembly is established, the recognition efficiency of the dust accumulation amount of the air conditioner assembly is greatly improved, and the filter assembly replacement early warning system can set corresponding threshold values by itself so that early warning of the dust accumulation amount of different degrees can be conducted.

The invention discloses a continuous zooming method for a double-camera network camera based on a three-dimensional reconstruction technology, and belongs to the technical field of cameras, and the method comprises the steps: collecting double-camera image data of the double-camera network camera, and carrying out the preprocessing of the data; performing feature extraction and matching on the double-shooting image data of the double-shooting network camera, and determining corresponding points in the double-shooting image data of the double-shooting network camera; parallax is calculated according to the corresponding points, depth information of each pixel in the scene is calculated according to the parallax, and a depth map is constructed; generating a scene three-dimensional model according to the depth map and internal and external parameters of the double-camera network camera; and simulating image effects under different focal lengths based on the scene three-dimensional model so as to realize continuous zooming. According to the invention, the problems that the image shooting effect is poor and the user experience feeling is reduced due to the fact that the existing double-camera network camera cannot be subjected to better continuous zooming are solved. According to the invention, continuous zooming can be carried out on the double-camera network camera, the image shooting effect of the double-camera network camera is improved, and the user experience can be improved.

The utility model belongs to the technical field of cameras, and particularly relates to a high-definition network camera which comprises a protective cylinder, a camera is arranged in the protective cylinder, sliding plates are fixedly connected to the two sides of the camera respectively, one end of each sliding plate is slidably connected with a sliding rail, one side of each sliding rail is fixedly connected with the protective cylinder, and the other side of each sliding rail is fixedly connected with the protective cylinder. The bottom of the camera is fixedly connected with a connecting rod, the bottom of the connecting rod is in sliding connection with the protection cylinder, the bottom of the connecting rod is fixedly connected with a connecting block, a push plate is arranged below the connecting block, clamping columns are arranged on the two sides of the connecting rod respectively, and the bottoms of the clamping columns penetrate through the connecting block and the push plate. Through the arrangement of the electric push rod, the push plate, the clamping column, the connecting block, the connecting rod, the sliding plate, the sliding rail and the protective cylinder, the effect of protecting the camera can be achieved.