88 results about "Pedestrian behavior" patented technology

The invention provides a vision detection system for vehicles not yielding to pedestrians at zebra crossing, comprising a monitoring camera covering the whole zebra crossing area, a HD (high definition) snapshot camera for taking snapshots of vehicles, a traffic cloud server, and a vision detection subsystem. The vision detection subsystem comprises a zebra crossing area detection module, a not-yielding-to-pedestrians behavior detection module, a not-yielding-to-pedestrians behavior vehicle image snapshot module, a vehicle type identification module, a license plate positioning and identifyingmodule, and a violation detection report generating module. The traffic cloud server is used for receiving video data of the whole zebra crossing area obtained by the monitoring camera, and submitting the video data to the vision detection subsystem to conduct not-yielding-to-pedestrians behavior detection and personal identity verification of violation vehicles. A violation detection report canbe automatically generated and stored with the detection and identification result, three snapshots of not-yielding-to-pedestrians vehicle images, and the process video images. The invention is quitehigh in detection precision and robustness.

The embodiment of the invention disclose a vehicle control method, equipment and a computer storage medium. The vehicle control method comprises the steps of acquiring first behavior data when a pedestrian gazes at a dangerous area which is a road area where a traveling target vehicle may collide with the pedestrian, inputting the first behavior data into a behavior prediction model which is obtained through training according to historical behavior data and historical behavior results so as to predict and obtain a behavior result of the pedestrian, and controlling the target vehicle to perform safe traveling according to the predicted behavior result. The vehicle control method, the equipment and the computer storage medium can solve the problem of inaccurate prediction of pedestrian behavior results and pedestrian motion paths and other problems in the prior art, thereby improving the safety, accuracy and practicability of vehicle control.

The invention relates to the field of intelligent transportation monitoring, and discloses a system and a method for snapshotting a behavior of motor vehicles not giving precedence to pedestrians on zebra stripes, by which no human cost is required, and an operation of carrying out automatic evidence-taking on illegal behaviors that motor vehicles do not give precedence to pedestrians is performed. According to the invention, the system comprises a pedestrian image acquisition unit, a motor vehicle image acquisition unit, a pedestrian detection unit, and a motor vehicle detection and snapshotting unit, wherein the pedestrian image acquisition unit is used for acquiring images of pedestrians on a zebra stripe; the motor vehicle image acquisition unit is used for acquiring images of motor vehicles passing cross a motorway on the zebra stripe; the pedestrian detection unit is used for detecting the number of pedestrians in the images of pedestrians; and the motor vehicle detection and snapshotting unit is used for carrying out real-time detection on motor vehicles according to the images of motor vehicles acquired by the motor vehicle image acquisition unit, and when detecting that a motor vehicle is located on the zebra stripe and the number of pedestrian is equal to or greater than a scheduled threshold, taking at least three evidence photos from the images of motor vehicles.

The invention discloses an active detection and recognition system and method for a human gait behavior based on semantic folding. Embedded gait behavior detection and recognition system hardware with low power consumption is built by using a three-dimensional sound field positioning system, a sole force field positioning system, an HDMI high-definition camera, a high-definition video acquisition system, a microcomputer Raspberry and the like. Gait semantic energy diagrams with timing characteristics provided by the invention contain gait time information under different situations, and the learning and prediction ability of a gait behavior cognition system can be enhanced according to a lot of gait semantic energy diagrams with timing characteristics. Meanwhile, the active detection and recognition technology for a human gait behavior based on semantic folding has extensive application prospects in various fields which mainly comprises the fields such as long-distance identity recognition, abnormal gait behavior detection, pedestrian behavior prediction and mass video retrieval, and has excellent economical and social benefits.

The invention relates to a pedestrian intention recognition method based on graph convolution. The video image of road environment is captured by a forward-looking camera system mounted on a vehicle.Pedestrian detection and pedestrian human key points extraction are carried out on the image, and the adjacency matrix is constructed to represent the connection information of pedestrian human key points based on graph theory. The bottom feature is extracted from coordinate information and adjacency matrix representation of key points by graph convolution algorithm, and the bottom feature is extracted and time series analyzed by depth convolution neural network and depth loop neural network. Based on the data set of pedestrian intention constructed by manual labeling method, the parameters ofthe model are optimized to realize the classification and recognition of pedestrian intention. The invention effectively utilizes the high-level semantic feature of the key point information of the pedestrian human body, so that the automobile advanced driving assistance system has the ability to understand the pedestrian behavior intention.

The invention discloses an assisted driving method and device, a vehicle-mounted terminal and vehicle. The method comprising: a step of acquiring an image sequence of a surrounding environment of thevehicle; a step of determining a pedestrian position and a pedestrian attribute in the image sequence, wherein the pedestrian attribute is used to represent a pedestrian category; a step of predictinga pedestrian movement trend and pedestrian behavior from the image sequence; a step of predicting pedestrian intent based on the pedestrian movement trend and the pedestrian behavior; a step of adopting a corresponding warning strategy based on the pedestrian location, the pedestrian attribute, and the pedestrian intent. The method can be used for automatically acquiring the image sequence of thesurrounding environment of the vehicle; the pedestrian position, the pedestrian attribute, the pedestrian movement trend and the pedestrian behavior can be obtained according to the image sequence; the pedestrian intent can be predicted according to the pedestrian movement trend and pedestrian behavior; finally the corresponding warning strategy is automatically adopted based on the pedestrian location, the pedestrian attribute, and the pedestrian intent to provide helpful assistance for the driver to drive safely, vehicle driving safety can be improved, and occurrence of traffic accidents can be reduced.

The invention discloses a behavior identification method based on a skeleton video. The behavior identification method comprises the steps: carrying out frame extraction on each segment of video in adata set, obtaining a video training sample, and extracting the skeleton joint point information of each frame; initializing a skeleton joint point connection relation matrix according to the skeletonjoint point physical connection relation so as to preliminarily train the spatial domain graph convolutional neural network; after the parameters of the spatial domain graph convolutional neural network are fixed, training a skeleton joint relation reasoning network through the spatial domain graph convolutional neural network to obtain a new skeleton joint connection relation matrix; updating parameters of the spatial domain graph convolutional neural network through the new skeleton joint point connection relation matrix; and during application, framing a to-be-identified video, extractingskeleton joint point information and then sending the skeleton joint point information to the skeleton joint point relation reasoning network to obtain a skeleton joint point connection relation matrix, and sending the framed video and the skeleton joint point connection relation matrix to the updated spatial domain graph convolutional neural network to obtain the pedestrian behavior category.

The invention discloses a pedestrian behavior recognition and trajectory tracking method, which comprises an image analysis algorithm and a trajectory analysis algorithm. collected video data sequentially passes through an image analysis algorithm module and a trajectory analysis algorithm module; The image analysis algorithm comprises the steps of data preprocessing, single-camera pedestrian trajectory tracking and cross-camera association, and camera abnormity recognition, and can achieve face detection, pedestrian detection and feature extraction; And a trajectory analysis algorithm includes calculating and analyzing trajectory data of the pedestrian from the acquired image, and identifying activity behaviors of the customer and the sales. The pedestrian behavior track can be monitoredand recognized in real time, the behavior state of pedestrians is intelligently judged, centralized management of the behavior track of customers and sales is facilitated, and more optimized and moreintelligent services are provided for the customers.

A method, apparatus, and computer program product are described herein for determining pedestrian behavior profiles for road segments of a road network, from those pedestrian behavior profiles, determining the likelihood that an adverse pedestrian event will occur, and determining the action to be taken in response. Example embodiments may provide a mapping system including: a memory having map data; and processing circuitry. The processing circuitry may be configured to: receive data points associated with pedestrian movement; associate pedestrian movement with a road segment; determine, based on the data points, a pedestrian behavior profile for the road segment; and in response to the pedestrian behavior profile for the road segment indicating a likelihood for an adverse pedestrian event that satisfies a predetermined likelihood, cause at least one action in response thereto.

The invention provides a multi-person abnormal behavior detection method based on security monitoring video data. According to the method, a standard AV output signal of a monitoring camera is acquired; pedestrian characteristics are extracted, and coarse detectors, coarse pedestrian ROIs and precise ROIs are respectively acquired; pedestrian behavior tracking is carried out, a particle filtering method is employed to respectively surround each tracking target of a video into a rectangular frame, a multi-order autoregression process mathematics model is established for state transferring of each tracking target, and a state transferring model for describing actual motion situations of motion targets is acquired; under the particle filtering framework, a particle filtering human body tracking method integrated with color and shape characteristics is acquired; abnormal-pedestrian classification is carried out, and optical flow characteristics of the precise ROIs are calculated; each frame of gray-scale image in the monitoring video flow is set to be a Markov random field ; characteristics of pedestrians determined to have abnormal traffic behaviors in video monitoring scenes are extracted, a continuous hidden Markov model is established, and the abnormal behaviors are identified.

The invention discloses a mobile phone signaling data pedestrian traffic track prediction method based on deep learning, and belongs to the mobile phone signaling data pedestrian traffic track prediction field; the method comprises the following steps: combining adjacent cells surrounding a traveler spot into a cell group, converting geological position adjacency into natural language similarity, wherein the geological position adjacent cells are highly likely selected by pedestrians as destinations like logical similar vocabularies; the cell group selection can greatly improve the travel mode excavation reliability and authenticity; the method can use the excellent performances obtained by a RNN network on natural language prediction, thus excavating traveler traveling habits mapped as language modes with high performance; according to the travel rule theoretical basis, the method can summarize the traveling modes, thus exceeding existing pedestrian behavior mode excavation method based on signaling data, and improving the performance by 42-49%.

The invention discloses a video-based pedestrian and crowd behavior identification method. The whole framework comprises a single-person limb part network, a single-person whole and limb joint networkand a multi-person network. The framework aims to jointly learn joint co-occurrence and time evolution in an end-to-end mode, and joint co-occurrence characteristics of skeleton sequence informationcan be learned simply and effectively by utilizing a CNN model by utilizing the global aggregation capability of the CNN. In the method, point-level features of each joint are learned independently, and then the features of each joint are regarded as channels of a convolution layer to learn hierarchical symbiotic features. Most importantly, in a designed single pedestrian behavior recognition joint network structure, multi-part limb network features are fused into single pedestrian motion features to enhance behavior recognition of a single pedestrian. Besides, in the designed crowd interaction behavior recognition network, the characteristics of the group behaviors are enhanced by utilizing the characteristics of the single person behaviors, and the group behaviors relate to activities such as embracing and handshaking of behaviors of multiple persons.

A method for identifying whether right-turning motor vehicles yield to pedestrians or not belongs to the field of traffic engineering and solves a problem that whether right-turning motor vehicles yield to pedestrians or not cannot be judged accurately in the prior art. The method is implemented based on a traffic monitoring system. A speed cut identification line is set behind a parking line of each right-turn lane. When motor vehicles turn right, the motor vehicles enter an area between the parking line and the speed cut identification line and two conflict areas in sequence. The method includes steps that traffic conditions of a right-turn lane and crosswalks are recorded in real time through a traffic monitoring system; when pedestrians in the crosswalk of the first conflict area has way right, the traffic monitoring system performs vehicle speed detection on the area between the parking line and the speed cut identification line and the first conflict area; when pedestrians on thecrosswalk of the second conflict area has way right, the traffic monitoring system performs vehicle speed detection on the second conflict area, so that whether the motor vehicles yield to the pedestrians or not can be determined. The method is mainly used for effective judgment on yield-to-the pedestrian behaviors.

The invention relates to a crossroad pedestrian crossing comprehensive management control system. The system comprises a camera capturing unit, a crossroad comprehensive management unit and an integrity management unit. The camera capturing unit is used for acquiring monitoring images of road sections and crossroads; the crossroad comprehensive management unit is used for receiving monitoring images transmitted by the camera capturing unit and storing and compressing the monitoring images; the integrity management unit is used for recognizing faces of pedestrians running the red light and comprises an event recording and storing module, an image receiving module, an automatic image recognition module, a manual image recognition module and an image medium recognition module, wherein the image receiving module, the automatic image recognition module, the manual image recognition module and the image medium recognition module are in sequential connection, and the image receiving module is connected with the crossroad comprehensive management unit. By real-time camera capturing of pedestrian quantity and pedestrian behavior states, pedestrians running the red light are recognized and recorded, and every uncivil behavior is recorded to maximally recognize pedestrians with uncivil behaviors, so that valid evidences can be provided for an illegal event management center to effectively control the bad habit of the pedestrians running the red light.

The present invention discloses a passenger characteristic analysis method for a track transportation junction. The method comprises the following steps: junction station pedestrian space demand analysis; junction station pedestrian flow macroscopic characteristic analysis; and pedestrian delay analysis. According to the analysis method disclosed by the present invention, pedestrian behavior characteristic theories, including a pedestrian flow microscopic theory, a pedestrian macroscopic theory and a statistic regression method, are applied to track transportation junction passenger characteristic analysis work, the pedestrian space demand is analyzed according to video information and an equation is obtained by means of curve fitting for the pedestrian flow macroscopic characteristic on different facilities, and a conclusion of whether a current facility meets a maximum traffic capacity or not is given out.

The embodiment of the invention provides a pedestrian track obtaining method and system. The method comprises the steps of obtaining pedestrian video information of a preset place; performing target detection on a target frame of the pedestrian video information by adopting a preset target detection algorithm to obtain pedestrian position information in the target frame; obtaining a multi-target tracking result corresponding to the target frame according to the pedestrian position information in the target frame and the multi-target tracking result of the previous frame of the target frame; and generating a pedestrian moving track according to the multi-target tracking result. According to the embodiment of the invention, the pedestrian movement track in the video can be accurately identified, the identification efficiency is high, the pedestrian movement track identification precision in video monitoring is improved, pedestrian behavior analysis is facilitated, and the monitoring efficiency is improved.

The invention relates to a monitoring video multi-dimensional pedestrian behavior intelligent analysis system and method. The system comprises an individual behavior analysis module, a small group behavior analysis module, a group behavior analysis module, a scene analysis module, a pedestrian behavior dimension evaluation module, an intelligent gateway, an application logic integration module anda video source management module. The method comprises the following steps: acquiring a monitoring video data stream, extracting video frames from the monitoring video data stream, and performing scene classification and pedestrian behavior dimension selection by using key video frame data; combining the scene and pedestrian behavior dimension information; performing decisions selected by the pedestrian behavior analysis module and transmission of management data; and performing intelligent behavior recognition through a pedestrian behavior analysis model of a scene integrated in the pedestrian behavior analysis module under the selected behavior dimension, and outputting a final pedestrian behavior recognition result. Pedestrian behavior recognition in various complex monitoring scenes and pedestrian behavior dimensions can be supported.

In order to solve the problem of low accuracy of behavior analysis in the technical problems, the invention provides a behavior analysis method and device based on human body key point detection, andthe accuracy of behavior analysis is improved. The behavior analysis method based on human body key point detection comprises the steps of detecting pedestrians in a to-be-identified video; tracking pedestrians in the detected to-be-identified video; extracting a human body key point sequence of pedestrians in the tracked to-be-identified video; and obtaining pedestrian behaviors based on the human body key point sequence and a preset behavior classifier, and the behavior classifier being a classifier obtained by training based on the human body key point sequence and the pedestrian behaviors.The device comprises a detection module, a tracking module, an extraction module and a judgment module. The pedestrian behavior is obtained based on the human body key point sequence and the preset behavior classifier, and compared with the prior art, the method has higher efficiency and accuracy.

The present invention discloses an intelligent automobile driving system based on the internet of things. The intelligent automobile driving system based on the internet of things comprises a processing module; a vehicle condition self-report module collecting self driving parameters; and a pedestrian behavior report module collecting the pedestrian behavior parameters; a vehicle condition outside transmission module transmitting the self driving parameters to automobiles around and / or a global positioning system; a vehicle condition receiving module receiving the driving parameters around; a flight device self report module collecting the flight parameters of a flight device; an object condition self report module collecting the position information of a moving or static object on the road; a monitoring module monitoring the states of the vehicles and pedestrians around; a processing module processing the collected information; and a people-vehicle safety movement module controlling the vehicles to execute safe driving motion according to the early warning determination of the safe early warning determination. Through adoption of the internet of things information technology and the artificial intelligence automation technology, the intelligent automobile driving system based on the internet of things is able to improve the processing and analysis accuracy, greatly enhance the driving safety and realize the effective management of the social traffic order.

The present invention relates to methods of predicting risk of collision depending on the behavior of a pedestrian adjacent to a driving vehicle and controlling the vehicle for preventing collision. According to an embodiment of the present invention, a method of controlling a vehicle comprises identifying a pedestrian adjacent to a driving road of the vehicle, determining a first recognition value of the pedestrian for the vehicle based on a behavior feature of the pedestrian, outputting a warning signal based on the first recognition value, determining a second recognition value of the pedestrian after outputting the warning signal, and controlling the vehicle based on the second recognition value.

The embodiment of the invention provides an automatic driving scene generation method and device and a storage medium. The method comprises the steps: inputting traffic network information and randompositioning points into a pedestrian behavior generator to generate an initial pedestrian trajectory; inputting the traffic network information and the random positioning points into an obstacle vehicle behavior generator to generate an initial obstacle vehicle track; according to a pedestrian behavior discriminator, discriminating the trueness of the initial pedestrian trajectory to obtain a first discrimination result; according to an obstacle vehicle behavior discriminator, discriminating the trueness of the initial obstacle vehicle trajectory to obtain a second discrimination result; obtaining a comprehensive discrimination value according to the first discrimination result and the second discrimination result; and if the comprehensive discrimination value does not accord with the preset condition, adjusting parameters of the pedestrian behavior generator and the obstacle vehicle behavior generator, and then re-executing the generation step and the discrimination step until the comprehensive discrimination value accords with the preset condition. According to the embodiment of the invention, the trueness of a simulation scene and the accuracy of a simulation test can be improved.

The invention discloses a safe and efficient traffic signal light management system and method. The system is provided with two infrared sensing modules which are arranged in the direction that pedestrians walk towards a zebra crossing in sequence, and the walking direction of the pedestrians can be judged according to the triggered sequence of the infrared sensing modules; if the far infrared sensing module is firstly triggered and the near infrared sensing module is triggered later, it shows that the pedestrians walk towards the zebra crossing, the behavior is valid, when the behavior is valid, traffic lights on a road and traffic lights for the pedestrians are changed, the light colors are changed, and the management of the traffic lights is achieved; if the near infrared sensing moduleis firstly triggered and the far infrared sensing module is triggered later, it shows that the pedestrians walk back on to the zebra crossing, and the behavior is invalid; the behaviors of the pedestrians are effectively identified, and invalid triggering is avoided. Besides, through the combination of a pressure-sensitive sensor, double insurance is achieved; the pedestrian behaviors are furthereffectively identified and invalid triggering is avoided (if the pedestrians just want to go to a crossing to look around instead of crossing the road, the pressure-sensitive sensor cannot be triggered) while the traffic efficiency is guaranteed. The safe and efficient traffic signal light management system improves the safety and the user-friendliness.

The invention provides a pedestrian motion trail detection and violation analysis system and relates to systems for trail detection and violation analysis during pedestrian crossing. A video collection module, a behavior trail processing module and a violation behavior analysis module are adopted to jointly form the pedestrian motion trail detection and violation analysis system, wherein the video collection module contains a high-definition camera, a tripod and a connecting line; the behavior trail processing module contains a data extraction module, a data conversion module and a trail description module for converting pedestrian behavior characteristic parameters into a pedestrian crossing trail graph; and the violation behavior analysis module contains a data extraction and conversion module capable of extracting traffic parameters in violation behaviors, a violation probability prediction module and a passage efficiency analysis module under different violation probabilities. Through the system, pedestrian crossing trail detection and crossing violation analysis are realized, a user can track and detect a pedestrian crossing trail by means of video collection and perform behavior analysis and expression, and therefore the convenience of pedestrian behavior analysis is improved.

The invention discloses an automotive external airbag and starting system with a surrounding pedestrian behavior prediction function. The automotive external airbag and starting system comprises a video analysis system, a radar system, an analytical processing system and an external airbag device. According to the automotive external airbag and starting system with the surrounding pedestrian behavior prediction function, by the technical scheme, a historical database is established, then the behavior of a surrounding pedestrian can be more accurately predicted by performing artificial neural network training on a current condition, and when collision is predicted to happen, an external airbag is started to effectively protect the pedestrian.

The invention discloses a multi-feature fusion series RNN structure and a pedestrian prediction method. The structure comprises an information acquisition module, an information processing module, a series GRU module, a full connection layer module, an activation function module and a prediction module. The information acquisition module acquires video images of pedestrians and the surrounding environment when a vehicle is driven in environments with different roads and crowd densities and the speed of the vehicle. The information processing module processes the acquired data to generate a data set; each stage of GRU in the series GRU module processes different information in the data set and inputs the hidden state of the previous stage of GRU connected in series, and carries out fusion calculation on the different information; the full connection layer module integrates the multi-dimensional matrix to obtain a one-dimensional vector; the excitation function module processes the one-dimensional vector information; and the prediction module obtains a prediction result of the pedestrian trajectory. According to the method, information from multiple sources is fused layer by layer indifferent neural network layers according to the complexity of the information, and pedestrian behavior understanding and track prediction are realized.

The invention discloses a crossing passenger behavior monitoring system which comprises the components of a signal lamp image acquisition module, a video analyzing module, a passenger behavior analyzing module and a crossing image acquiring and processing module. The signal lamp image acquisition module is used for acquiring a signal lamp image in real time. The video analyzing module is used for determining the state of the signal lamp according to the image which is acquired by the signal lamp image acquisition module and transmits a passing forbidding signal to the passenger behavior analyzing module and the crossing image acquiring and processing module. The crossing image acquiring and processing module is used for photographing the video image of the crossing, processing the video image and transmitting the processed image to the passenger behavior analyzing module. The passenger behavior analyzing module is used for acquiring the processed image of the crossing image acquiring and processing module after the passing forbidding signal is received, capturing and tracking the passenger and determining whether the behavior of the passenger violates an order. The crossing passenger behavior monitoring system greatly reduces labor cost in performing artificial monitoring on the crossing.

The invention relates to a pedestrian behavior simulation method in microscopic traffic simulation, which comprises the following steps of: firstly, using an agent technology and a high level architecture / run-time infrastructure to build a traffic environment model and various behavior atom model sets; secondly, building a managing and managed logic relationship between traffic entity agents and road sections and between the traffic entity agents and the crossings of the road sections; then, through the communication between pedestrian agents and the traffic environment, acquiring traffic environment information, conducting knowledge filtration to the information, recombining the information into internal conceptual representation, conducting reasoning to the environment and self-desires through a belief-desire-intention technology and obtaining a further behavior plan required to be executed by pedestrians; and finally, executing the plan and forming a simulation step size to realize the effective simulation of pedestrian behaviors. The method can accelerate the generation of the behavior plan of the simulation system and the reasoned pedestrian characteristics are more fit to the simulation of the pedestrian behaviors.

Aspects of the disclosure relate to detecting sidewalks adjacent to roads. In this regard, a set of potential sidewalk areas adjacent to one or more roads in a vehicle's vicinity may be determined based on map data. Topology data for the set of potential sidewalk areas may be generated based on sensor data received from a perception system of the vehicle. The set of potential sidewalks may be filtered to remove areas unlikely to include a sidewalk. The vehicle may be operated based on the filtered set of potential sidewalk areas, which may include taking precautionary measures when within a predetermined distance from any of the filtered set of potential sidewalks.

The invention relates to the field of intelligent transportation monitoring, and discloses a system and a method for snapshotting a behavior of motor vehicles not giving precedence to pedestrians on zebra stripes, by which no human cost is required, and an operation of carrying out automatic evidence-taking on illegal behaviors that motor vehicles do not give precedence to pedestrians is performed. According to the invention, the system comprises a pedestrian image acquisition unit, a motor vehicle image acquisition unit, a pedestrian detection unit, and a motor vehicle detection and snapshotting unit, wherein the pedestrian image acquisition unit is used for acquiring images of pedestrians on a zebra stripe; the motor vehicle image acquisition unit is used for acquiring images of motor vehicles passing cross a motorway on the zebra stripe; the pedestrian detection unit is used for detecting the number of pedestrians in the images of pedestrians; and the motor vehicle detection and snapshotting unit is used for carrying out real-time detection on motor vehicles according to the images of motor vehicles acquired by the motor vehicle image acquisition unit, and when detecting that a motor vehicle is located on the zebra stripe and the number of pedestrian is equal to or greater than a scheduled threshold, taking at least three evidence photos from the images of motor vehicles.

The invention discloses an intelligent traffic pedestrian behavior monitoring alarm system based on big data. The system comprises vehicle terminals corresponding to vehicles one by one, intersectionmonitoring terminals arranged at intersections and a cloud computation server terminal; through collecting the panoramic image in a large range, the condition that whether an illegal behavior occurs is judged, and meanwhile, appearance information of a party is generated. The system can be matched with the existing automobile data recorder to collecting multi-angle and near distance images. Compared with the common intersection monitoring camera, the automobile data recorder widely applied to the vehicle is gathered into the whole image collection system, so that the image sources are wider, meanwhile, the update cost also can be greatly reduced, and facial feature is extracted from multiple multi-angle and near distance videos at the time of an incident instead of being extracted from theremote and single-angle video, so that collection of the facial feature of the party is clearer and more accurate, the different alarm strategies are made and executed according to perniciousness, and the accident can be timely and effectively processed.