110 results about "Vehicle counting" patented technology

The embodiment of the invention discloses a method and device for counting vehicles, relating to the field of intelligent traffic monitoring and overcoming the problem of inaccurate vehicle counting. The method comprises the following steps of: determining a counting mode of the vehicles according to correlation coefficients corresponding to a gray scale histogram of a video image; in the daytime mode, carrying out motion detection and / or edge detection by utilizing a preset first virtual coil and vehicle head detection by utilizing a preset second virtual coil, and further identifying the vehicles in the video image and counting the vehicles; and in the night mode, carrying out head light detection and head light matching by utilizing a preset third virtual coil, and further identifying the vehicles in the video image and counting the vehicles. According to the embodiment of the invention, the counting modes of vehicle counting are determined according to the correlation coefficients corresponding to the gray scale histogram of the video image, the counting modes comprise the daytime mode and the night mode, and the corresponding method for counting the vehicles is called according to the determined counting mode, therefore, the accuracy rate of the vehicle counting is improved.

The invention provides a magnetic resistant vehicle quantity and speed detecting node based on a Zigbee wireless protocol, which comprises a shell, wherein a core processing plate and a power supply cell connected with the core processing plate are arranged in the shell; and a vehicle signal acquiring unit, a signal processing unit and a wireless communication unit which are connected are arranged on the core processing plate. The invention acquires the passing information of vehicles by the vehicle signal acquiring unit and finishes the corresponding functions of vehicle counting and vehicle speed measurement by the signal processing unit and the wireless communication unit. The invention also provides use methods of the magnetic resistant vehicle quantity and speed detecting node based on the Zigbee wireless protocol, which comprises a method for singly using the detecting node, a method for using the detecting node in pairs and a method for using the detecting node in a networking way. The detecting node can be singly used, used in pairs and used in the networking way and is convenient for finishing the corresponding functions of the vehicle counting and the vehicle speed measurement.

Disclosed is a vehicle counting method based on video pictures, comprising steps as follows: catching a multi-frame video picture and collecting the pixel value of each pixel in the multi-frame video picture, beginning from the preset frame picture to calculate the probability density value of the pixel value at the preset time of each pixel in the preset frame video picture according to each collected pixel value in the multi-frame picture, confirming the foreground picture of the video picture via utilizing the probability density value by calculating, confirming the area in the foreground picture being larger than a certain area of specified value as motive vehicles, and running after the vehicles in the video picture to obtain the number of new vehicle and that of departed vehicles. The invention can be carried out by a network camera, a microprocessor, a video codec and hardware system of computers. The method of the invention employs computer vision technology to realize vehicle counting, further, the invention is simple in required equipment, easy in realizing and low in cost.

The invention discloses a video vehicle detection method for adaptive learning. The video vehicle detection method for the adaptive learning treats a video vehicle detection problem as a mode classifying problem, mainly comprises an image feature extracting step, a classifier off-line training step, a classifier on-line optimizing step and a vehicle counting step, and comprises the following specific steps of firstly extracting a plurality of discriminative image features from a monitoring video, wherein the image features can be used for discriminating vehicles and backgrounds and also comprise environment information associated with light and weather conditions; secondly off-line training a mode classifier by utilizing a supervised learning method, and also online optimizing the mode classifier to automatically adjust the structure and the parameter of each component classifier, so that the classifier has the adaptive learning capability and the better classifying effect is obtained in a complex traffic scene; and finally carrying out post-process on a classifying result sequence to further improve the vehicle detecting and counting precision. The video vehicle detection method for the adaptive learning disclosed by the invention has the advantages of reinforcing the traditional virtual coil vehicle detection method, having a remarkable engineering application value and being capable of facilitating the development of the video monitoring field and the intelligent traffic field.

The invention discloses a vehicle counting-based traffic light control system and a control method thereof. The system comprises a video monitoring information acquisition module, a video monitoring information comprehensive analysis and decision module, a traffic light control module and a traffic light controlled implementation module, wherein the video monitoring information acquisition module is used for acquiring a real-time traffic flow monitoring frame; the video monitoring information comprehensive analysis and decision module performs vehicle counting on real-time traffic flow by a statistical learning-based vehicle detection method and combines a vehicle counting result with the perennial traffic flow statistical result of an actual road to generate a traffic light control decision; and the traffic light control module is used for sending a traffic light state variation instruction to the traffic light controlled implementation module according to the traffic light control decision. The accuracy of the vehicle counting is improved by perfecting the program of the vehicle detection method under the condition of not changing external equipment, and the system has the characteristics of low updating cost and high function expandability.

The invention provides an electric automobile charging recommendation system and method. The system comprises a smart phone terminal of an electric automobile user, a WeChat public platform, a charging management platform, a map service platform and a plurality of charging stations in a set region. The WeChat public platform is provided with a WeChat official account; a charging pile in each charging station is equipped with a scanning device; and a charging station entrance / exit is provided with a vehicle counting device. When the system is used, the user logs in the WeChat official account and sends a charging request to the charging management platform through the WeChat public platform; and the charging management platform receives the charging request and analyzes user related information, searches the charging stations in the reachable range of the electric automobile and real-time information thereof according to built-in GIS information and called real-time road condition information query and navigation service of the map service platform and recommends the charging station and charging time most meeting the user charging requirements to the user. The system can effectively meet diversified automobile charging requirements of the users.

The invention relates to a method for detecting vehicle flow of multiple lanes on the basis of video analysis, and belongs to the field of acquisition of traffic parameters of intelligent traffic systems. The technical scheme includes that the method for detecting the vehicle flow of the multiple lanes on the basis of video analysis includes steps of building video background models; setting transverse main virtual detection lines, auxiliary virtual detection lines and longitudinal virtual detection lines in video backgrounds; preprocessing video; building background image models by the aid of Gaussian mixture background models and detecting vehicle targets; determining whether vehicles need to be counted or not, in other words, determining whether change of relations between the vehicle targets is available in image data of each local frame on the corresponding main virtual detection line and image data of a next frame or not, according to the background image models of front and rear frames when the moving vehicles enter the main virtual detection lines; carrying out statistics on vehicle counting according to vehicle target detection results. The transverse main virtual detection lines and the auxiliary virtual detection lines are positioned on the lanes, the longitudinal virtual detection lines are positioned at the edges of the lanes, and parallel distances from the main virtual detection lines to the auxiliary virtual detection lines are fixed.

The invention discloses a vehicle flow statistics method based on a virtual detection line, and the method is applied in an actual road traffic scene, wherein real-time traffic video flows are processed; and moving objects are identified and extracted by a frame difference method. With regard to vehicle flow detection, problems such as detection omission and wrong detection will inevitably take place to a vehicle counting method based on the virtual detection line. Aiming at the problem, the invention extracts and combines two types of image information including position information and pixel variation information and discloses a novel vehicle flow division counting method based on the virtual detection line. The method combines respective advantages of a virtual coil and target tracking and has the advantages of timeliness and high accuracy of vehicle flow statistics. Experimental results show that accuracy of the method used in video vehicle counting in each lane under various different weather conditions exceeds 95%, and the method has the advantages of easy generalization and implementation.

The invention provides an intelligent urban road dynamic regulation system based on the Internet-of-Things technology and belongs to the field of intelligent transportation. The intelligent urban road dynamic regulation system based on the Internet-of-Things technology is mainly used for congestion management of urban traffic. The intelligent urban road dynamic regulation system based on the Internet-of-Things technology is characterized by comprising a motor vehicle flow monitoring and processing system, a traffic signal dynamic indication system and a lane dynamic isolation system. On the condition that the urban road bi-directional motor vehicle flow is unbalanced seriously, the intelligent urban road dynamic regulation system can conduct dynamic intervention and distribution on one lane or more lanes to achieve quick vehicle diverging to the maximum extent and avoid traffic congestion. In the specific implementation process, a motor vehicle counting sensor is arranged on each lane and used for collecting motor vehicle flow information. The collected data information is processed and then transmitted to a traffic control center. The control center can make a judgment according to the processed data information. If the lanes need to be regulated dynamically, the control center will conduct remote intervention control over the traffic signal indication system and the lane isolation system so as to achieve dynamic regulation and distribution on the lanes.

A computing device, for example, receives image data of vehicles on a path captured by a camera. The image is analyzed using a low level feature extraction on the image data. The computing device estimates exhaust levels produced by the vehicles based on the low level feature extraction or based on vehicle classifications and quantities determined from the low level feature extraction.

The invention discloses a vehicle counting method based on movement track, and relates to the field of traffic detection. The method of the invention comprises the following steps: stable feature points are selected and tracked; clustering is carried out according to the change of relationship between the same-time relative distances of the track of the stable feature points in a real space; and the count value of a vehicle counter is not updated when the variance of the same-time relative distances is smaller than a preset threshold, and 1 is added to the count value of the vehicle counter when the variance of the same-time relative distances is greater than the preset threshold. According to the invention, the vehicle track in a shot road video image is extracted and processed to obtain the relative distances of the track at the same time, and the number of vehicles in the video image is determined according to the change relationship between the same-time relative distances. Therefore, the influence of the environment in the vehicle counting process is avoided, the limitation that only vehicles of a single model can be counted is eliminated, and the precision and efficiency of vehicle counting are improved.

The invention discloses a deep learning classification method based on feature screening. An accurate image classification model can be obtained by training and testing a data set by using the deep learning classification method based on feature screening provided by the invention. According to the method, accurate classification of the images can be efficiently completed, the number (dimensionality reduction) of the features can be reduced through a feature screening method under the conditions that serious over-fitting, redundancy, much noise and the like exist between the features and the target, and therefore the generalization ability of the model is improved to obtain higher classification accuracy. In an image classification task, data and features determine the upper limit of machine learning, and a model and an algorithm only approach the upper limit. Therefore, the feature engineering plays a very important role in machine learning. The classification of the input data can bepredicted, so that the method is widely applied to the fields of traffic scene object recognition, vehicle counting, license plate recognition and internet and biomedical image analysis in the security field and the traffic field.

Traffic density is estimated around a host vehicle moving on a roadway. An object detection system remotely senses and identifies the positions of nearby vehicles. A controller a) predicts a path of a host lane being driven by the host vehicle, b) bins the nearby vehicles into a plurality of lanes including the host lane and one or more adjacent lanes flanking the predicted path, c) determines a host lane distance in response to a position of a farthest vehicle that is binned to the host lane, d) determines an adjacent lane distance in response to a difference between a closest position in an adjacent lane that is within the field of view and a position of a farthest vehicle binned to the adjacent lane, and e) indicates a traffic density in response to a ratio between a count of the binned vehicles and a sum of the distances.

The invention discloses a vision-based density traffic vehicle counting and traffic flow calculation method and system. The vision-based density traffic vehicle counting and traffic flow calculation method comprises the steps of zooming an acquired continuous frame image to obtain a pyramid characteristic map, inputting the pyramid characteristic map to a trained pyramid-YOLO network to detect vehicle targets with different scales so as to obtain a boundary frame with the vehicle targets; presetting a line-crossing probability function, judging probability of each target vehicle passing through a counting line in an image with the vehicle targets, and sieving a traced vehicle according to a probability value; performing tracking track processing on the traced vehicle according to a limitation-based multi-target tracing algorithm, and counting the vehicles passing through the counting line according to an obtained track set; and calculating to obtain vehicle flow traffic volume, speed and density according to the obtained vehicle counting result. By the vision-based density traffic vehicle counting and traffic flow calculation method, the problem of a dense traffic scene is solved,a system on the basis of vehicle detection, tracing, counting and parameter estimation is proposed, vehicle detection is accurately and rapidly performed, the dense vehicle is accurately traced, and flow estimation is achieved.

The invention discloses a road side parking low-high-position video fusion detection method based on geomagnetic radar detection. The method comprises the following steps that equipment initializing is carried out; a microprocessor is controlled to input signals collected by all the components into a data acquisition unit; the data acquisition unit is used for converting the acquired signals into digital signals and inputting the digital signals into the microprocessor; the microprocessor analyzes and calculates the received digital signals to obtain vehicle judgment results and parking event information of the parking spaces respectively; and the microprocessor is used for transferring the calculated vehicle counting information and the parking event information to a storage unit and sending the vehicle counting information and the parking event information to a receiving terminal through a wireless transmitting module. According to the method, vehicle information is detected through the principle of multi-element sensor information monitoring based on geomagnetic radars and low-high-position video fusion, the basic information of the vehicle can be collected, the detection precision can be effectively improved, and misdetection can be avoided.

The invention discloses a vehicle detection method based on target feature points, which belongs to the field of image processing. The method comprises steps: a transition matrix between a world coordinate system and a pixel coordinate system is acquired; a moving target area in a video is determined, feature points are extracted from the moving target area, pedal points are further determined, stable feature points are selected from the video, the stable feature points are tracked, a moving target track line is acquired, track line judgment is carried out according to the moving target track line, if the two track lines are judged to belong to the same vehicle, one is added to a vehicle counting result, and the above steps are repeated until the video is over. Through introducing a detection mode based on target feature point tracking and clustering to vehicle detection, compared with the prior art, environmental restrictions are reduced, stability and the detection precision are high, detection errors are reduced, and vehicle recognition accuracy is improved.

The invention discloses a highway pavement settlement monitoring and early warning system, which belongs to the technical field of settlement monitoring and early warning. The highway pavement settlement monitoring and early warning system comprises a settlement detection unit, an auxiliary detection device, a relay device and a monitoring center, wherein the settlement detection unit comprises multiple displacement sensors, and the multiple displacement sensors are located at the lower end of the pavement and located in a roadbed; the auxiliary detection device comprises a video acquisition device, an automobile weighing device, a vehicle counting device and an earthquake detection device; the earthquake detection device is located at the lower end of the pavement and located in the roadbed; and the relay device comprises a data acquisition unit, a wireless transmission unit and an emergency communication device. The settlement of the highway pavement can be monitored in real time; due to acquisition of multiple data, a worker can analyze the causes of pavement settlement in many ways more conveniently, comprehensive understanding of monitored road sections by a monitoring centeris realized, and hidden safety hazards caused by settlement of the highway can be eliminated in time.

The invention provides a night traffic flow detection method based on headlamp trajectory tracking and dynamic matching. The method comprises the steps of: video collection; parameter input and calibration; static extraction: through preprocessing, acquiring image brightness information and segmenting out all light spots and preliminarily screening out headlamp light spots based on geometric features; dynamic filtering: tracking the light spots obtained through static extraction and removing disturbing light sources based on the geometric features in the tracking process: vehicle lamp matching: smoothing original tracking trajectories and performing vehicle lamp matching based on smoothed trajectory feature similarity and trajectory point space position relationships; vehicle counting: when vehicle lamps are tracked to a traffic flow detection line position, counting vehicles according to a vehicle lamp matching result; traffic information output. The method extracts headlamp light spots according to the time domain stability of the brightness feature and the geometric features of headlamps at night and performs vehicle lamp matching based on vehicle lamp tracking trajectories, thereby being capable of counting vehicles in complicated night road scenarios.

The embodiment of the invention provides a vehicle counting method and device. The vehicle counting method comprises the steps that vehicle targets in current image information are determined according to the acquired current image information; when the vehicle targets are in the current image information, vehicle targets on a preset standard line in the vehicle targets are taken as vehicle targets to be counted; when at least one vehicle target is included in the vehicle targets to be counted, the previous frame image information of the current image information is used to determine the actual count value in the vehicle targets to be counted; and the sum of the actual count value and the current total count value is taken as the new total count value. An image analysis algorithm is introduced into traffic monitoring video data processing, and the video data are analyzed and processed to automatically detect and count the number of vehicles. Real-time monitoring of the road traffic flow is realized. The method is suitable for vehicle counting under complicated road conditions, and has the advantage of simple and effective operation.

The invention discloses a time-space image-based vehicle counting method applied to an urban traffic scene. Firstly, an initialization method of a background model is provided; the traffic states of the pixel points of a current scene are judged; the confidence of pixel points in the background model is calculated, and whether the background model is updated is judged; the background model is updated according to a threshold self-adaption updating method used for a current traffic state, and a pixel-based self-adaption segmentation method is utilized to detect foreground; the results of foreground and background detection on a virtual detection straight line are maintained accumulatively so as to form a time-space image; and morphological filtering processing is performed on the time-space image, so that connected regions can be obtained, and the number of the connected regions is calculated, so that the number of vehicles can be obtained. With the method of the invention adopted, the problem of vehicle counting in a complex scene can be solved, and counting can be more accurate. The method is simple and is high in detection speed.

The invention discloses a vehicle counting method based on three-dimensional trajectory clustering. The method specifically comprises steps of: acquiring a video image of a road by using a video camera, establishing a relation between a two-dimensional image coordinate and a three-dimensional world coordinate by using a vanishing point method, and acquiring a transfer matrix M; determining a detection line and a detection area in the video image and extracting a background image of the video image; acquiring multiple moving trajectories in the video image; performing coarse clustering on selected moving trajectories satisfying a coarse clustering condition, classifying the selected moving trajectories into multiple categories, and marking the selected moving trajectories as clustered categories; and performing fine clustering on the clustered moving trajectories marked in the step 4. The vehicle counting method is not restricted to environment in engineering application, has high stability and detection precision, is easy to implement and capable of detecting vehicles within the scope accurately in real time, and has wide application prospect.

The invention discloses a method for counting vehicles in lanes based on a full convolutional network, which comprises the following steps of: s1, setting a monitoring detection area, extracting videoframe information, performing lane division processing on the video frame information, and setting an interested area in each lane area, the interested area being an area needing to be monitored anddetected in each lane; s2, processing the video frame information to obtain a vehicle target of the current monitoring image, and outputting a vehicle density map; s3, according to the vehicle densitymap in the step S2 and the lane area in the step S1, obtaining the number of vehicles in any lane; s4, presetting a threshold value yi for the number Ni of vehicles which can be accommodated in the lane i; if Ni is greater than yi at any time within the time T, starting speed judgment; s5, constructing 3D convolution, and obtaining the average speed vi of vehicles in the lane i within a short time t; and S6, judging whether the lane i is congested or not by combining the step S3 and the step S5. Video frames can be processed efficiently in real time, vehicle counting is carried out, and the circulation condition of a road is judged.

The invention discloses an RFID-based UAV vehicle counting method, which comprises the following steps that an AMS system compiles a counting instruction; according to the counting instruction, the counting task is automatically generated and sent to the control system of unmanned aerial vehicle; the UAV control system receives the count task and executes it; the RFID code reader reads the code ofeach location to be counted in the counting task; the UAV control system judges the result of each code reading and enters into different program; the counting information contained in each code reading result is fed back to the AMS system in real time and verified; after completing the current counting task, whether the new counting task is received or not is determined, if so, the counting taskis continued, otherwise the UAV is controlled to return to the rest area; the AMS system generates the location information table according to the inventory record and publishes it to the IWMS system. The remarkable effect is that the automated counting of the whole warehouse vehicle is realized, and the problems of heavy workload and inaccurate counting of the whole manual vehicle are overcome.

The invention discloses a dispatching system based on a logistics conveying device. The dispatching system comprises a communication server, a shuttle vehicle module and a goods shelf module. The communication server comprises a counter, a central processing unit and a storage unit. The goods shelf module comprises two parallel guide rails, and laser transmitting plates are evenly distributed on the side faces of the guider rails. The shuttle vehicle module comprises a shuttle vehicle, a laser sensor, a speed information receiving unit, a shuttle vehicle control unit and a shuttle vehicle counting unit. The speed information receiving unit receives speed information sent by the central processing unit. The laser sensor is arranged on the shuttle vehicle, the driving distance is recorded through laser pulses transmitted by the laser transmitting plates, meanwhile, the central processing unit is used for conducting general control over the driving speed of the shuttle vehicle, collisions are avoided, goods can be intelligently and automatically stacked, the space is reasonably utilized, the safety coefficient of the shuttle vehicle system is increased, and the work efficiency is improved.

The invention discloses a dense vehicle detection method based on vehicle counting perception attention, which is characterized in that vehicles in a dense environment are detected based on a deep learning network model, the network model comprises a vehicle number perception network model and a dense target detection network model, and the two network models are integrated through a vehicle counting perception attention module. Effective features of vehicle detection are enhanced, semantic information and spatial information of vehicles are effectively represented by constructing a vehicle counting density map, a vehicle number perception loss function based on cross entropy is calculated, the model is supervised to learn the semantic information and the spatial information of the vehicles, and the vehicle detection effect of the network model is improved.

The invention discloses a deep learning vehicle counting method based on road surface extraction and segmentation. The method specifically comprises: collecting video images of a road by using a camera; adopting a digital image processing method to acquire a road surface area and adopting a segmentation strategy to segment a road surface into a near end part and a far end part; sending the segmented road surface area into a deep learning network to detect a vehicle target, carrying out continuous tracking according to a detection result to obtain a vehicle two-dimensional trajectory, and counting the flow of different types of vehicles in a certain road direction by using the vehicle two-dimensional trajectory to achieve the purpose of vehicle counting. According to the method, the detection precision of small vehicles far away from the road surface is high, and a data basis is provided for accurate vehicle counting. The method can be applied to various traffic scenes, has high stability and counting precision, can effectively and accurately detect and continuously track vehicles in a road range in an image visual field so as to realize vehicle counting, and has a wide applicationprospect.

The invention provides a vehicle counting method and system based on a multiplex detection area. The method comprises the steps of aiming at a current frame for counting vehicles in a target traffic video, obtaining the state of a first detection area and the state of a second detection area of a target lane in the current frame; if the first detection area is in an occupied state and the second detection area is in an idle state, according to the time sequence of the frame, judging whether subsequent frames of the current frame satisfy a first preset condition or not in sequence until the subsequent frame satisfying the first preset condition is obtained, wherein the first preset condition is that the first detection area of the target lane is in the idle state and the second detection area is in the occupied state; adding 1 to the counting value of the vehicles in the first direction of the target lane, and regarding the next frame which is adjacent to the subsequent frame and satisfies the first preset condition as the next current frame for vehicle counting. By means of the vehicle counting method and system, the accuracy of vehicle counting is improved, and the influence of shadows is lowered.

The invention belongs to the field of data, and relates to intelligent traffic, in particular to a traffic flow statistics and vehicle type classification method and device. The method comprises the following steps: acquiring a traffic flow video, setting a rectangular identification frame in a video image sequence, and taking a frame without vehicle passing in the identification frame as a background frame; obtaining a differential image of the current frame and the background frame; storing the gray scale average value in a gray scale feature array; calculating a vehicle entry threshold value and a vehicle driving-out threshold value in the statistical time window; comparing the gray average value with the two thresholds respectively, and if the gray average value is greater than the entry threshold, adding 1 to the count of a vehicle counter and freezing the counter; if the gray average value is smaller than the driving-out threshold value, enabling a counter; and finally obtainingtraffic flow data; if the time duration is greater than the vehicle entry threshold, adding 1 to the counter, and resetting the timer; if the time duration is smaller than the vehicle driving-out threshold , freezing the timer; storing the vehicle model characteristic data into a vehicle model characteristic array; and calculating classification thresholds, comparing the vehicle model characteristic values with the classification thresholds, and finally obtaining a vehicle model classification result.

The invention discloses a rail guide vehicle control method based on a laser sensor. The method comprises: a rail guide vehicle sending full-load information to a central processing unit after loading; the central processing unit receiving the full-load information and then acquiring counting information stored in a storage unit; the central processing unit sending acceleration information to a speed information receiving unit; the speed information receiving unit receiving the acceleration information and then feeding back the acceleration information to a rail guide vehicle control unit; a rail guide vehicle counting unit sending the quantity of laser reflecting plates to the central processing unit; the central processing unit sending a counting signal to a counter, and the counter increasing one to the quantity of goods laid on a shelf module. The rail guide vehicle is provided with the laser sensor, the travel distance is recorded via laser pulse emitted by the laser reflecting plates, and the central processing unit simultaneously controls the running speed of the rail guide vehicle, so that collision is avoided, goods can be intelligently and automatically stacked, the space is reasonably utilized, the safety factory of a rail guide vehicle system is increased, and the working efficiency is improved.