42 results about "Traffic camera" patented technology

The invention discloses a road section illegal parking space-time prediction system based on deep learning, and relates to the technical field of intelligent traffic management, and the system comprises a multi-source data fusion collection module, a space-time self-attention feature extraction module, a dynamic granularity parameter generation module, a space-time hybrid model prediction module and an illegal parking early warning visualization module. By collecting traffic camera video streams, vehicle-mounted sensor data, mobile phone signaling data and meteorological data in real time, comprehensive perception of the traffic environment is achieved, the multi-source heterogeneous data are mapped to a unified GIS grid after being subjected to space-time coordinate calibration, a multi-dimensional illegal parking influence factor database is constructed, and on the basis, the real-time monitoring of the illegal parking influence factor database is achieved. The space-time self-attention feature extraction module performs efficient space-time feature extraction on multi-source data by using a space-time diagram self-attention model, generates feature vectors with space-time relevance, and solves the problems of single data and insufficient feature extraction in a traditional method.

The invention relates to the technical field of large models, in particular to an urban intelligent collaborative decision-making system and method based on a large model, and the system comprises a multi-source heterogeneous data fusion perception layer, a deep learning cognitive engine, a self-adaptive regulation and control decision-making layer and a system efficiency optimization and safety guarantee system. The method has the advantages that multi-modal data of a traffic camera, a geomagnetic sensor, a vehicle-mounted terminal and the like are integrated, space-time correlation characteristics are extracted by using a multi-layer attention mechanism of a large model, and traffic flow prediction and signal timing optimization linkage regulation and control are realized in combination with an incremental learning algorithm. Compared with the prior art, the platform solves the problems that a traditional method is poor in dynamic adaptability to a complex road network and low in prediction precision, the passing efficiency of the urban road network can be improved by 15%-22%, and the abnormal congestion response time is shortened to be within 3 minutes.

The invention relates to the technical field of traffic management, and discloses a traffic decision-making system and method based on multi-source meteorological data fusion, and the system comprises a data collection module which obtains meteorological station observation data, satellite remote sensing data and meteorological information in a traffic camera image in real time; the preprocessing module is used for carrying out space-time alignment and standardization processing on the meteorological information to generate preprocessed meteorological data with a unified space-time reference; the multi-source fusion module is used for performing cross-modal feature extraction and correlation analysis on the preprocessed meteorological data based on a feature level fusion model of deep learning to generate a fused meteorological feature map; and the decision generation module inputs the fused meteorological characteristic spectrum into a pre-trained traffic decision neural network model, and synchronously outputs a traffic scheduling instruction and a safety management instruction. According to the invention, efficient fusion of multi-source meteorological data can be realized, and the accuracy and effect of traffic scheduling and safety management decisions are improved, so that the traffic scheduling and safety management decisions can better cope with complex and changeable traffic scenes.

The invention discloses a non-motor vehicle passenger-vehicle identification method and system in a crowded scene, and belongs to the technical field of intelligent traffic management. In order to solve the problem of accurate recognition of non-motor vehicles, persons and vehicles in a crowded scene, image data of a traffic intersection is collected through a traffic camera, target detection processing is performed on the image data by using a target detection model, and a person target and a non-motor vehicle target are detected to obtain a person target frame and a non-motor vehicle target frame; judging whether the scene category to which the image data of the traffic intersection belongs is a crowded scene or not to obtain crowded scene image data; personnel key points and non-motor vehicle key points in the crowded scene image data are extracted; associating the personnel key points with the non-motor vehicle key points to determine personnel key points associated with the non-motor vehicle key points to obtain a personnel-vehicle association target; and identifying whether the person-vehicle associated target has traffic violation behaviors or not through an image tracking technology, and if so, identifying the identity information of the person target for traffic violation early warning and processing.

The invention discloses a road infrastructure real-time monitoring method and system based on an unmanned aerial vehicle Internet of Things, and relates to the technical field of road monitoring, and the method comprises the steps: carrying a laser radar, a high-resolution camera, an infrared thermal imager and a vibration sensor through an unmanned aerial vehicle group, synchronously collecting the geometric, texture, temperature and vibration data of a road infrastructure, and in combination with environmental data of a meteorological station and a traffic camera, a multi-modal data stream is constructed. Deviation caused by unmanned aerial vehicle attitude and sensor heterogeneity is eliminated through a space-time alignment algorithm, rain and fog interference is eliminated by using dark channel prior, an atmospheric scattering model and a generative adversarial network, and then fracture geometric parameters are extracted. A correlation graph is constructed based on a graph neural network, node features and edge weights are fused, and risk grades are divided by calculating the fatigue life remaining probability of the infrastructure and combining damage indexes. And finally, a predictive maintenance decision is generated, and a full-link closed loop from data acquisition and processing to decision is realized.

The invention discloses a light supplement lamp and a light supplement method thereof, and the light supplement method comprises the steps: obtaining real-time echo information which is fed back by a monitoring radar and covers a light supplement area, and obtaining a real-time image which is fed back by a traffic camera and covers the light supplement area when the real-time echo information indicates that there is a vehicle in the light supplement area; respectively processing the real-time echo information and the real-time image to obtain vehicle prediction information and shading area information; a light control scheme is generated based on the obtained light control mode, the vehicle prediction information and the shading area information, and the working states of the multiple lighting sources are adjusted based on the generated light control scheme; according to the light supplementing method disclosed by the invention, the real-time echo information is processed to predict the vehicle track, and the shading area information is obtained in combination with the real-time image, so that the turned-off lighting source and the shading area information can be ensured to be consistent in a dynamic process, and the predictive light control improves the road lighting efficiency and the energy-saving property; and meanwhile, the driving safety is obviously enhanced.

Electronic display systems, including roadside display devices, vehicle-based devices, personal mobile devices, intermediary servers, advertising servers and / or networks, and / or additional external data sources may operate individually or in combination to identify one or more vehicle locations, driving routes, driver and passenger characteristics, driving behavior and patterns, telematics data, and the like. Vehicle and individual characteristics and / or telematics data may be determined based on data received from traffic cameras, vehicle-based devices, personal mobile devices, telematics devices, and / or other data sources, including software applications. Based on the vehicle characteristics, individual characteristics, driving data and driving patterns, telematics data, and the like, content, such as digital display content or audio content, may be determined for output on various devices, such as electronic roadside displays to be viewable by the approaching vehicles, and / or other devices to be accessible by associated individuals via the other devices. Various techniques may be used to determine customized content. Additionally, certain systems may be interactive to allow user responses and follow-up content via on-board vehicle devices or other user devices.

The invention relates to the technical field of fire safety management, and particularly discloses a community fire rescue path optimization method and system, and the method comprises the steps: building a community map database through employing a geographic information system, collecting the traffic flow data of each road segment through employing a traffic camera, and then, carrying out the collection of the traffic flow data; performing time sequence analysis on the traffic flow data of each road section by using a data processing technology based on deep learning so as to capture a traffic flow time sequence dynamic change trend of each road section, and further performing global association coding on traffic flow time sequence change characteristics of each road section according to a communication relationship among the road sections of the community so as to obtain traffic flow time sequence data; therefore, the overall traffic road condition mode of the community is revealed, and the optimization recommendation of the rescue route is realized on the basis. Through the mode, real-time optimization of a community fire rescue path can be realized, the fire response speed is improved, and the loss caused by a fire disaster is reduced.

The invention provides a multi-agent-based heterogeneous traffic camera cooperative control method, and belongs to the technical field of reinforcement learning and edge computing. The method comprises the following steps: in a target selection stage, an agent combines available computing resources with target uncertainty data to generate an embedded vector; in the control participation stage, the control participation module receives the embedded vector, extracts the coarse-grained characteristics of the embedded vector through the multi-head self-attention block, and transmits the coarse-grained characteristics to the action participation selection block; the action participation selection block determines whether the agent should participate in the task at the current time step so as to save the calculation time; in the rotation control stage, a rotation control module of the intelligent agent extracts fine-grained features through double self-attention blocks, and the fine-grained features are used for decision making of a rotation action selection block, and the optimal rotation action is determined to reduce the target uncertainty. According to the invention, decision time and target uncertainty are minimized at the same time, rotation of a plurality of traffic cameras is intelligently controlled, and a plurality of moving targets are tracked.

The invention discloses a road construction area intelligent sealing control and scheduling method fused with edge calculation, and relates to the technical field of road construction traffic management, and the method comprises the steps: S1, data collection and fusion: deploying edge calculation nodes at the periphery of a road construction area, and enabling the edge calculation nodes to access and fuse data collected by a multi-source sensor in real time, the data comprises traffic flow data acquired by a traffic camera, traffic flow speed data measured by a millimeter wave radar, global positioning system (GPS) positioning data of a construction vehicle, and visibility and precipitation data acquired by a weather sensor. By fusing edge calculation and multi-source sensor data, the traffic flow change can be responded in real time, the sealing control range is shrunk in the peak to reduce congestion, and road resources are reasonably adjusted and prevented from being idle in the flat peak; the device can adapt to weather condition changes, a safe buffer area is expanded in low-visibility environments such as rainstorm and heavy fog, and the traffic capacity is recovered in time when the weather turns good.

The utility model relates to a device facilitating disassembly, maintenance and cleaning of traffic cameras and an information acquisition system. The traffic camera dismounting, maintaining and cleaning device comprises a base, a transmission shell is fixedly installed at the top end of the base, a stand column is fixedly installed in the middle of the top end of the transmission shell, and a transverse rod is transversely arranged on one side of the top end of the stand column; the hanging bracket is fixedly mounted at the bottom end of the cross rod, a camera body is movably mounted at the bottom end of the hanging bracket, and a positioning mechanism is arranged between the camera body and the hanging bracket; a winding mechanism is arranged between the stand column and the camera body, a spraying mechanism is arranged on the left side of the middle of the stand column, and a driving mechanism is arranged in the transmission shell. According to the utility model, the camera body is convenient to disassemble or maintain, the position of the lens is adjusted through the system of the camera body, so that the lens faces the nozzle and is convenient to clean.

The present invention proposes a multi-agent-based heterogeneous traffic camera collaborative control method, which belongs to the fields of reinforcement learning technology and edge computing technology. The method includes: in the target selection stage, the agent combines its available computing resources with the target uncertainty data to generate an embedding vector; in the participation control stage, the participation control module receives the embedding vector, extracts the coarse-grained features of the embedding vector through the multi-head self-attention block, and transmits the coarse-grained features to the participation action selection block; the participation action selection block decides whether the agent should participate in the task at the current time step to save computing time; in the rotation control stage, the rotation control module of the agent extracts fine-grained features through the dual self-attention block for the rotation action selection block to determine the optimal rotation action to reduce target uncertainty. The present invention simultaneously minimizes decision-making time and target uncertainty, intelligently controls the rotation of multiple traffic cameras, and tracks multiple moving targets.

The present invention discloses a method for predicting risks in a tunnel based on sensor data analysis, which relates to the technical field of tunnel risk prediction. The method includes arranging sensors in the tunnel for data collection, analyzing the data collected by the sensors to perform a comprehensive anomaly score, setting a prediction optimization threshold for a machine learning model to determine tunnel anomalies; and making a determination based on the tunnel anomalies. The method of the present invention comprehensively combines the characteristic values of each sensor by means of weighted summation, and dynamically adjusts the threshold in cooperation with the machine learning model, so that the multi-dimensional sensing data maintains an efficient anomaly detection ability. By comprehensively evaluating the traffic conditions through the monitoring data of traffic cameras and combining the sensor data, multi-level data fusion and analysis are realized, reflecting the environmental changes in the tunnel, ensuring the comprehensiveness and accuracy of risk assessment, providing strong decision-making support for tunnel management and risk warning, and enabling the warning measures based on risk warning to respond quickly to ensure the safety of vehicles and personnel.

A rainfall inversion algorithm based on transfer learning includes the following steps: dataset preparation, dataset classification, dataset preprocessing, and construction of a multivariate Gaussian mixture model featuring grayscale changes and saturation, further filtering candidate raindrops within the ROI region. The raindrops obtained after the above filtering steps are used to invert rainfall amounts using a Gamma distribution model based on the definition of raindrop spectra. This invention, due to the adoption of the above technical solution, has the following advantages: it can effectively invert rainfall amounts using videos captured by traffic cameras without relying on other professional rainfall monitoring equipment; it has stronger anti-interference performance in complex environments; it reduces the probability of misjudging rainfall in sunny environments; and it has greater practical significance and application value.

The present invention provides a method, device and electronic device for constructing a pseudo 3D bounding box, which relate to the technical field of image processing. By acquiring video data of a traffic scene collected by a traffic camera and extracting a vehicle image corresponding to a target vehicle from the video data, a pseudo 3D bounding box corresponding to the target vehicle is constructed based on a body 2D detection frame, an upper body 2D detection frame and a vehicle-end 2D detection frame in the collected vehicle image. The technical solution can be realized only by relying on 2D visual detection technology, and its technical principle is relatively mature and reliable, the actual operation is simple, the technical cost is low, and the application scenarios are wide.

The invention provides a traffic violation behavior identification method based on multi-source data fusion. The method comprises the following steps: acquiring multi-source data in a vehicle driving process through a plurality of sensors; fusing the multi-source data according to a preset rule, and outputting the fused multi-source data; and based on the fused multi-source data, identifying the traffic violation behavior of the vehicle. According to the invention, the data of the recorder, the traffic camera and the V2X communication equipment are fused, so that the problems of limited identification precision, especially high false alarm rate in a complex traffic environment and the like due to the fact that most of the prior art depends on a single data source are solved.

This application relates to the field of information technology and discloses a multi-source heterogeneous data fusion and analysis platform for smart cities. It includes a data acquisition end error calibration module, a transmission link integrity assurance module, a multi-source data fusion and cleaning module, and a dynamic timeliness management module. Each module achieves data flow through a unified data interaction protocol. The acquisition end is used to collect data from traffic cameras and environmental monitoring sensors. The data acquisition end error calibration module is used to calibrate errors generated by the data collected by the acquisition end. The acquisition end error calibration module also includes an environmental adaptive sensing unit and a device health assessment unit. Through the synergistic effect of the environmental adaptive sensing unit and the device health assessment unit, data reliability is improved from the source. The environmental adaptive sensing unit uses a pure hardware electrical signal transmission link to achieve real-time response to changes in visibility and illumination.

The invention discloses a method and a system for predicting red light running behaviors of pedestrians in a sparse scene, and belongs to the technical field of intelligent traffic control. In order to solve the problem that pedestrian red light running behaviors in a sparse scene are judged in advance, a first area and a second area are arranged in a traffic camera acquisition range of a traffic intersection; performing real-time target detection processing on the first area, performing static behavior detection on the pedestrian by using a behavior detection model to determine a target pedestrian, and obtaining an image sequence of the target pedestrian in the first area and an image sequence of the target pedestrian in the second area; performing trajectory extraction to obtain trajectory data of the target pedestrian, and performing behavior extraction to obtain a behavior sequence of the target pedestrian; according to the speed trend of the trajectory data of the target pedestrian, calculating a behavior probability value of the target pedestrian to judge whether the target pedestrian is a red light running intention, and constructing a positive sample and a negative sample of red light running; and constructing a red light running prediction model to obtain a trained red light running prediction model for predicting the red light running behavior of the pedestrian in the sparse scene.

The invention relates to the field of intelligent traffic, in particular to an urban traffic flow monitoring and predicting system based on deep learning, and the system comprises a data acquisition module which is used for reading the data of each frame in a traffic camera through a pre-trained target detection algorithm, and carrying out the recognition of human and vehicle targets; performing target tracking on an identification result obtained by the target detection algorithm by using a tracking algorithm so as to obtain a time sequence of the pedestrian flow; and the traffic flow prediction module is used for predicting the pedestrian and traffic flow according to a pre-trained prediction model, and the pre-trained prediction model is obtained by training the time sequence of the pedestrian and traffic flow obtained by the data acquisition module. According to the intelligent traffic monitoring and prediction system based on deep learning, the limitation of a traditional traffic management mode is broken through, and the intelligent level of traffic flow monitoring and prediction is remarkably improved.

The invention discloses a traffic violation detection and insurance acceptance automation method, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: synchronously collecting multi-source video stream data in a vehicle driving process through a vehicle-mounted camera and a traffic camera, the time-space consistency alignment of the video streams is realized based on the timestamp and the space coordinate mapping; transmitting the aligned multi-source video stream to a cloud server, and carrying out preprocessing, target detection and behavior analysis on the video stream by using a multi-modal analysis model based on deep learning to judge whether a traffic violation behavior exists or not; generating a violation record containing violation time, place, type and vehicle information according to the violation judgment result, and uploading the violation record to a cloud server for storage through a data compression and encryption technology; and based on the violation record, an insurance acceptance process of an insurance company system is automatically triggered through a standardized API interface, insurance auditing and compensation processing are completed, and a processing result is fed back to a user side and a traffic management department.

The invention provides a traffic monitoring video processing method and system, and the method comprises the steps: starting timing when a traffic camera located on a road is detected to start to work in real time, and carrying out the segmentation processing of a traffic monitoring video generated in real time at preset time intervals, so as to generate a plurality of corresponding sub-videos; the sub-videos are analyzed to detect a starting moment, an intermediate moment and a termination moment corresponding to the sub-videos in real time, and the starting moment, the intermediate moment and the termination moment all comprise specific numerical values; generating an encryption key corresponding to the sub-video in real time according to the starting moment, the intermediate moment and the ending moment based on a preset rule, and encrypting the sub-video through the encryption key to generate a corresponding encrypted video in real time; and generating a corresponding target folder in real time based on the encrypted video, and correspondingly storing the encrypted video into the target folder. According to the invention, the secrecy performance of the video can be improved, and the working efficiency is correspondingly improved.

The invention discloses a spherical camera for traffic management, and relates to the technical field of traffic cameras, the spherical camera comprises mounting seats, a protective shell and a detection controller, the mounting seats are symmetrically and fixedly connected to the outer wall of the protective shell, and the detection controller is fixedly mounted at the bottom end of the protective shell. A moisture-proof cleaning mechanism is arranged at the top of the protective shell, the transparent glass shell is matched with the camera in size, and the transparent glass shell can be driven to rotate through meshing transmission of a bevel toothed plate and a bevel gear, so that a polluted part is rotated into the protective shell to be cleaned, and the cleaned part is synchronously rotated out to a shooting position; the monitoring view field of the camera is not shielded in the whole process, continuous and stable pictures are guaranteed, the cleaning brush and the dryer only act on the part, transferred into the protective shell, of the non-monitoring area of the transparent glass shell, and interference of cleaning action on real-time monitoring pictures is avoided in cooperation with accurate transmission control of the transmission wheel and the conveying belt; and the integrity of video data required by traffic management is ensured.

The present invention discloses a real-time monitoring method and system for highway infrastructure based on the UAV Internet of Things, which relates to the field of highway monitoring technology, including: using a group of drones equipped with laser radars, high-resolution cameras, infrared thermal imagers and vibration sensors to synchronously collect geometric, texture, temperature and vibration data of highway infrastructure, and combining the environmental data of weather stations and traffic cameras to construct a multimodal data stream. The deviation caused by the heterogeneity of drone posture and sensors is eliminated by a spatiotemporal alignment algorithm, and the interference of rain and fog is eliminated by using dark channel priors, atmospheric scattering models and adversarial generative networks, and then the geometric parameters of cracks are extracted. Based on a graph neural network, an association map is constructed, and node features and edge weights are integrated. The remaining probability of fatigue life of the infrastructure is calculated and the risk level is divided in combination with the damage index. Finally, a predictive maintenance decision is generated to achieve a full-link closed loop from data collection, processing to decision-making.

The invention relates to the technical field of city management, in particular to a smart city data processing method and system based on Beidou positioning, and the method comprises the following steps: collecting Beidou positioning data and traffic camera records of vehicles in city traffic, associating the position coordinates and timestamps of the vehicles with speed data, generating a vehicle operation state database, and storing the vehicle operation state database in a cloud server; and carrying out time sequence coding on the vehicle operation data in the vehicle operation state database to generate traffic flow time sequence analysis data. Through the sliding time window and the Kalman filtering, the prediction accuracy and the dynamic monitoring capability are improved. Through combination of vehicle positioning and camera data, the real-time analysis efficiency of the traffic flow is improved, and the timeliness and practicability of the data are enhanced through time sequence coding. Furthermore, in combination with greening area image data and geographical location information of the unmanned aerial vehicle, the scientificity of urban greening management is enhanced through normalized vegetation index analysis.

The utility model discloses a traffic monitoring camera convenient to overhaul, which relates to the technical field of traffic monitoring systems and comprises a transverse mounting rod inserted into a longitudinal upright post. The camera assembly is installed on the transverse installation rod, the transverse installation rod is located on a road conveniently, traffic cameras are arranged and installed in the direction perpendicular to the driving direction of the road, road information can be monitored comprehensively and accurately, use is convenient and stable, and the longitudinal assembly can control the transverse assembly to rotate and ascend and descend. Therefore, subsequent overhaul, maintenance, replacement and other operations of the camera assembly are facilitated, climbing operation and road closing are not needed, and the problems that an existing monitoring camera is not provided with any lifting structure, climbing operation is needed during subsequent overhaul and maintenance and is troublesome, and the arrangement direction of the monitoring camera is generally perpendicular to the driving direction of the road, so that the safety of the monitoring camera is influenced are solved. Therefore, the road needs to be blocked during maintenance, and the traffic rate of the road is reduced.

Systems and methods for detecting potential theft and identifying individuals having a history of committing theft use an electromagnetic emission associated with a personal electronic device associated with an individual is received from at least one of a sensor that is coupled to at least one of a traffic camera or an aerial drone camera. One or more signal properties of the electromagnetic emission are analyzed to determine an emission signature. Video data and video analytics are correlated with the emission signature to identify the individual having possession of the item. The emission signature and video data are stored for later use during a checkout procedure. If an emission signature detected at a checkout station matches that of the individual having possession of the item, and the item is not processed through the checkout station, an alert is issued and the individual is flagged as a potential shoplifter.

The invention provides an intersection view blind area vehicle tracking method based on a fusion perception technology, and the method comprises the steps: carrying out the vehicle recognition of the video data of each path collected by a multi-path traffic camera at an intersection through a target detection algorithm, and obtaining a recognition result, which is the image coordinate of a vehicle on each path; the recognition result is processed through an image and reality coordinate conversion method, and real world coordinates of each vehicle are obtained; based on the real world coordinates, vehicles are mapped to an intersection overlook map one by one through the mapping relation between the overlook map coordinates and the real world coordinates, and an intersection target level fusion map is obtained; and carrying out target tracking on the vehicles at the intersection based on the positions in the intersection target-level fusion map, and sending tracking data to the target vehicles waiting at the intersection. With the adoption of the scheme, vehicle tracking in the view blind area of the intersection is realized, and collision avoidance early warning and traffic safety at the intersection are effectively promoted.

The disclosure provides a traffic camera, a method and device for identifying a target object, and relates to the technical field of image processing, and particularly to the field of intelligent traffic. The method is applied to the traffic camera, and the traffic camera comprises at least one image sensor and a first circuit board. The specific implementation scheme is that the at least one image sensor is controlled to collect a target image, wherein the display content in the target image comprises a target object to be identified; the target image is subjected to image recognition processing on the first circuit board to determine whether the target object has an abnormal traffic behavior; and when the target object has the abnormal traffic behavior, traffic anomaly information is reported to a target server.

The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and / or an externally-generated event.