156 results about "Traffic camera" patented technology

A mounting assembly for supporting a traffic camera or other traffic control device high above traffic. The assembly includes a conventional camera bracket, a vertical pole of at least two inches in diameter, an adapter for attaching the camera bracket to the top of the pole, and a mounting bracket for attaching the pole to the mast arm or other support. The bracket includes an adapter plate by which the pole can be rotated relative to the support so the camera can be mounted on either a vertical or a horizontal support structure. The bracket also includes a clamp that attaches to the support structure by a three point attachment. This versatile mounting assembly can support a traffic control device 10-15 feet above the mast arm or other support, without significant movement of the pole relative to the bracket or of the bracket relative to the support.

A navigation device displays a route superimposed on a road map, together with one or more symbols each indicating a location for which audio or visual traffic information exists, each symbol being selectable to play back the audio or visual information. Visual traffic information can be from a fixed traffic camera; a driver can hence rapidly view a video feed or still image from a traffic camera to allow him to assess the traffic conditions.

A multiobjective management system for saturated traffic road networks comprising: green wave coordination of locally adaptive traffic control units, traffic movement optimization and live traffic route guidance. Current traffic congestion measurements on intersections are generated from local traffic cameras and remote air-borne conventional cameras and thermal sensing imaging cameras or satellite radar such as SAR/ISAR using optical image brightness analysis. At the first stage of traffic optimization, individual local intersection green times are computed based on current traffic congestion level. At the second stage optimization, the central traffic server uses a multiobjective approach to coordinate the current locally-optimized green times of the first stage and create input constraints for green-way coordination of plurality of traffic lights. The server updates dynamically current cycle start and green times on all network-connected traffic light controllers and also broadcasts recommended travel times, green times and green waves to all on-line client vehicle navigation units. Traffic server and individual client guidance units utilize novel time-dependent modifications of an A*-type algorithm to update current travel and recommended travel times and to execute fastest route searches.

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 realizing method for comparing radiofrequency identification and license plate identification comprises the following conditions that if a reader-writer identifies an electronic label firstly, license plate information in the label is kept for a certain time period t1, in the time period t1, a electronic logo license plate number is constantly compared with all license plate numbers identified by a snapshot camera, if a traffic camera snapshots a vehicle image and identifies the license plate information firstly, the license plate number identified by the traffic camera is kept for a time period t2, and the license plate number is constantly compared with the all electronic logo information obtained by the reader-writer in the time period t2; if the matching is successful before the stop of any of the t1 time period or t2 time period, finishing the comparison, terminating the keeping time t1 and t2 and outputting a comparison result; if the matching is not successful yet before the stop of the any time period t1 or t2, finishing the comparison and outputting other comparison results.

Lane level traffic levels are determined based on traffic camera images. A controller aligns a three-dimensional map with a traffic camera view, and identifies multiple lanes in the traffic camera view based on lane delineations of the three-dimensional map. The controller calculates a traffic parameter based on the multiple lanes in image frames from the traffic camera view and provides a traffic graphic based on the traffic parameter.

The invention discloses a deep learning based automatic checking method against a vehicle lane-pressing illegal behavior. The method comprises the following steps that a snapshot picture of a camera is obtained, cut and ordered; the license plate number of a target vehicle is obtained; a deep learning based target vehicle detection module is used to detect the target vehicle in different ordered images, and a target vehicle detection frame is obtained; scene cutting is carried out on the ordered image by means of deep learning based scene cutting module, and segmented solid line pixels are obtained; in each ordered image, a vehicle lane pressing illegal behavior determining module determines whether there is an intersection point between a solid line fitting straight line and a straight line of a lower frame of the target vehicle detection frame by calculation; and whether the target vehicle in the group of ordered images has a lane pressing illegal behavior is determined according toposition of the intersection point. Thus, the method is suitable for illegal behavior checking via the pictures shot by traffic cameras in the real scene.

Methods and systems are disclosed for updating camera geometric calibration utilizing scene analysis. Geometric calibration parameters can be derived with respect to one or more cameras and selected reference points of interest identified from a scene acquired by one or more of such cameras. The camera geometric calibration parameters can be applied to image coordinates of the selected reference points of interest to provide real-world coordinates at a time of initial calibration of the camera(s). A subset of a video stream from the camera(s) can then be analyzed to identify features of a current scene captured by the camera(s) that match the selected reference points of interest and provide a current update of the camera geometric calibration parameters with respect to the current scene.

The invention discloses a traffic jam state prediction and early warning system and method based on city portraits. The system comprises the following modules: a data collection and processing module,a deep learning module, a historical record module and a front-end display module. Recording road condition information of each street and intersection in real time by utilizing data provided by a traffic camera; streets are abstracted into edges in an undirected graph, intersections are abstracted into points in the undirected graph, road condition information of the streets and the intersections is sorted into edge weights and point weights, the edge weights and the point weights are input into a deep learning model of a dynamic graph, a real-time city portrait is constructed, city trafficcongestion state propagation at the next moment is predicted, and early warning information is reported to a congestion area.

A vehicle type recognition method based on CNN multiple-features combining and multi-core sparse representation includes the steps of using a smart traffic camera to take a vehicle image under complex scene, conducting pretreatment for the vehicle image, designing five convolutional layers and three full connection layers and automatically extracting global and local characteristics of the vehicle based on AlexNet network in CNN, taking the feature maps of the first, second and fifth pooling layers and sixth and seventh full connection layers subjected to column stretching as vehicle feature components and inputting to five different single-core functions, forming a combined feature matrix through weighted fusion, solving the weight of each function and the projection matrix of the combined feature matrix, and realizing vehicle type recognition according to core sparse minimal reconstruction error. Real-time vehicle type identification can be realized, traffic flow information needed can be extracted, traffic jam problem can be solved, and general planning and road construction of a road network are facilitated.

The invention provides a road condition camera based vehicular navigation method and system and a vehicular terminal. The method includes steps: when the vehicular terminal starts, predicting a driving destination according to historical driving paths; planning at least two paths to the destination according to the predicted destination; on the basis of a received road condition checking request aiming at a certain road section in the planned paths, receiving and displaying real-time road condition images generated by a camera arranged at the road section; on the basis of received navigation path selection information, performing path navigation. The road condition camera based vehicular navigation method and system and the vehicular terminal have advantages that during path planning, congestion conditions of planned paths can be checked through cameras arranged on the paths, so that an appropriate path can be selected for navigation, and user experience is further improved.

The invention relates to an intelligent traffic camera, and discloses a method and a device for automatically controlling local lighting amount of the intelligent traffic camera. The intelligent traffic camera comprises a filter set, wherein the filter set comprises a first filter and a second filter; the first filter is located above the second filter; the transmission rate of the first filter is smaller than the transmission rate of the second filter. The method for automatically controlling the local lighting amount of the intelligent traffic camera comprises the following step of when the lighting intensity of the scene of a current video frame is judged to be lower than a threshold value, moving the filter set, so that the first filter and the second filter respectively cover one part of area of the whole scene, and the first filter covers the area where traffic lights are located. The filter set is controlled to move to the appropriate position according to different scene modes, and when other modes are switched to the low-light scene mode, the lighting amount of the traffic light area is limited, and the aim of avoiding overexposure of red lights is achieved, so that the problem that the red lights are a little yellow is avoided.

A method, system, and computer-usable tangible storage device for traffic camera diagnostics via strategic use of moving test targets are disclosed. The disclosed embodiments can comprise four modules: Moving test target management module, Moving test target detection and identification module, Image/video feature extraction module, and Sensor characterization and diagnostics module. A first test vehicle can travel periodically through traffic camera(s) of interest. The traffic camera(s) would then identify these test vehicles via matching of license plate numbers and then identify test targets in video frames through pattern matching or barcode reading. The identified test targets are then analyzed to extract image and video features that can be used for sensor characterization, sensor health assessment, and sensor diagnostics. The disclosed embodiments provide for a non-traffic-stop (i.e., non-traffic-interruption) traffic camera diagnostics.

When performing video-based speed enforcement a main camera and a secondary RGB traffic camera are employed to provide improved accuracy of speed measurement and improved evidentiary photo quality compared to single camera approaches. The RGB traffic camera provides sparse secondary video data at a lower cost than a conventional stereo camera. The sparse stereo processing is performed using the main camera data and the sparse RGB camera data to estimate a height of one or more tracked vehicle features, which in turn is used to improve speed estimate accuracy. By using secondary video, spatio-temporally sparse stereo processing is enabled specifically for estimating the height of a vehicle feature above the road surface.

The invention discloses a traffic camera with a radio frequency recognition function. The traffic camera is characterized in that an ultrahigh frequency RFID (Radio Frequency Identification) reader-writer is arranged in a universal traffic video recognition camera, and recognition functions of both parties are integrated and key data of both parties are shared; information of electronic vehicle identities (or called as electronic license plates) and numbers of vehicle license plates are collected and comprehensively compared, data and comparison results are generated and output by a data interface. Meanwhile, all functions of the electronic license plate system and the traffic camera can be respectively completed. The traffic camera with the radio frequency recognition function comprises a video recognition camera module unit, a video recognition camera imaging unit (including a lens), a radio frequency recognition read-write module unit, a bistatic signal process and comparison module unit and a relevant peripheral circuit and interface unit.

A method for determining a response to misalignment of a camera monitoring a desired area includes acquiring temporal related frames from the camera including a reference frame. A pixel location is determined of a reference object from the frames. Using the pixel location of the reference object, a displacement of the camera between a current frame and the reference frame is determined. For the displacement exceeding a first threshold, a new displacement of the camera is measured by introducing at least one additional object to a camera field of view and comparing the new displacement to a second threshold. For the new displacement not exceeding the second threshold, the camera is recalibrated using a determined pixel location and a physical location of the at least one additional object. For the new displacement exceeding the second threshold, notification is provided of a misalignment to an associated user device.

The invention discloses a vehicle re-identification method based on multiple branches. The vehicle re-identification method comprises the following steps: transmitting a vehicle to-be-identified imagethrough a traffic camera; inputting the to-be-identified image into a training model, obtaining feature vectors corresponding to the plurality of branches, specifically, giving the input vehicle image, an RAM (Random Access Memory) generating a group of function vectors, in particular, generating a feature map M by five shared convolutional layers, then feeding M to four branches to generate different features, wherein the four branches comprise a global branch, a BN branch, an attribute branch and a local area branch, and extracting corresponding feature vectors from the four branches; and comparing the feature vector of the to-be-identified image with the feature vector of the vehicle of the video image in the effective geographic area range, searching the vehicle target image with thehighest similarity by using a similarity calculation formula, and outputting a final re-identification system model. According to the method, the specific target vehicle can be tracked and found, andrelated departments are assisted to improve the target vehicle searching efficiency.

The invention provides a radio frequency identification and video identification comparison method and a radio frequency identification and video identification comparison system. The radio frequency identification and video identification comparison method comprises the following steps: acquiring first vehicle information collected by a traffic camera; acquiring second vehicle information and a corresponding RSSI value returned by a vehicle electronic identifier through a radio frequency identification reader-writer; according to the RSSI value returned by the vehicle electronic identifier, selecting second vehicle information returned by the vehicle electronic identifier when a vehicle corresponding to the vehicle electronic identifier is within the identification scope of the traffic camera; and determining whether the vehicle information matches by comparing the first vehicle information with the second vehicle information. The efficiency of comparison between radio frequency identification and video identification is increased. The method and the system adapt to normal comparison in various situations.

The invention relates to a method and device for snapshot control of a traffic camera. The method comprises the following steps of: obtaining an alternating current signal having the same power supply with a signal light; carrying out photoelectric isolation on the alternating current signal to produce a synchronous signal so as to obtain a signal light phase; and triggering the camera at the set phase according to the synchronous signal for snapshot. With the adoption of the method and device, the condition that signal light blackout and yellowing occur in pictures of rule-violating events shot by the camera when a stroboscopic signal light is used is eliminated.

The invention discloses a vehicle dynamic path navigation method for obtaining road condition video information in real time. The method comprises a navigation device firstly calculating at least one navigation path from the starting point to the destination according to the topological relation of the road nodes in navigation data; then, matching traffic camera information to the navigation path, calculating the path distance from each traffic camera point to vehicle current point, and meanwhile, carrying out ranking based on the path distance from each traffic camera point to the vehicle current point; and at last, obtaining and displaying real-time dynamic traffic video pictures. The invention also discloses a vehicle dynamic path navigation device for obtaining the road condition video information in real time. The device comprises a path programming module, a traffic camera data matching module, a dynamic traffic video displaying module and a comprehensive control module. Therefore, the traffic circulation efficiency of a road network can be effectively improved, and the problem of traffic jam is greatly improved.

A traffic control system and a method of automatic zone creation and modification for a smart traffic camera to be used in adaptive traffic management at an intersection are disclosed. One aspect of the present disclosure is a method including applying default zone parameters to define detection zones at one or more sensors installed at an intersection, the detection zones being used by the one or more sensors for monitoring and detecting traffic conditions at the intersection; determining a current vehicular traffic flow rate and a current pedestrian traffic flow rate at the intersection; determining if a triggering condition for adjusting one or more of the default zone parameters; and adjusting the one or more of the default zone parameters if the triggering condition is met.

The invention provides a traffic video processing device and method, and a retrieval device and method. The traffic video processing device comprises a unit extracting a retrieval object from traffic video data; a unit building a topological index relation of a video shot by an adjacent traffic camera; a unit dividing the video shot by the traffic camera into a plurality of frame clusters; a unit building an index relation of a frame cluster having semantic feature correlation in the video having the topological index relation and shot by the adjacent traffic camera; and a unit building a topological index relation of all videos by using the topological index relation unit, and building the index relation of the frame clusters of all videos based on the topological index relation and frame cluster index relation of all videos to form a video index database. Based on the traffic video processing device and method, and the retrieval device and method, provided by the invention, unnecessary workloads of retrieving the traffic videos during the traffic video retrieval can be reduced, and the influence on retrieval accuracy caused by unrelated videos can be prevented.

Provided is an application device for traffic camera traffic information acquisition based on image identification. The application device comprises an image acquisition module, a digital image processing module, a data transmission module and an electronic map display module four parts. The image acquisition module is used for collecting vehicle image information through traffic cameras arranged at the entrance and the exit of a target road section; the digital image processing module is used for converting the received vehicle image signals collected by the image acquisition module into digital signals through a virtual coil; the data transmission module is used for transmitting the digital signals obtained through conversion of the digital image processing module in a wireless manner; and the electronic map display module receives the signals transmitted by the data transmission module through a receiver, and displays information of on-road vehicles on an electronic map in a dot matrix manner. The device realizes analysis and processing of real-time traffic information collected by the cameras by adopting a dynamic image processing method and utilizing algorithms of virtual coil and the like, and allows the traffic information to be visually displayed on the electronic map.