CN205608812U - Public transport passenger flow measures detecting system based on face identification and position location - Google Patents

Abstract

The utility model discloses a bus passenger flow detection system based on face recognition and position positioning. The system includes an image acquisition module connected in sequence, a bus position acquisition module, a data mining center and a data analysis center; the image acquisition module is used for The facial images of getting on and off the bus are collected, the bus position collection module is used to record the driving trajectory of the bus, the data mining center is used to analyze the faces and positions of the collected images, and the data analysis center is used to According to the results obtained from the analysis of the data mining center, traffic statistics and analysis are carried out to provide decision-making basis for bus line planning, operation management, and line adjustment. The application of the utility model not only improves the detection accuracy of the bus passenger flow, but also improves the frequency of the bus passenger flow detection, and greatly saves the flow investigation cost.

Description

Bus Passenger Flow Detection System Based on Face Recognition and Location Location

technical field

The utility model relates to a bus passenger flow detection system based on face recognition and position positioning.

Background technique

Due to the uneven distribution of time and space for urban residents' trips, it will inevitably lead to very crowded buses at some stations, and even stranded passengers at the stations, or the full load rate of buses at some stations is not high. The above phenomenon is related to the rationality of the bus line network and its operation plan. Their data base is the bus passenger flow, including getting on and off at the station, cross-section flow (number of passengers in the bus) and bus OD (boarding from a certain station and another number of people getting off at one stop). In order to ensure the implementation of the public transport priority national development strategy, rationally organize limited transport capacity and timely evacuate passenger flow, the statistical analysis of bus passenger flow has attracted more and more attention and attention from traffic control departments and bus companies.

At present, there are two main methods of collecting bus passenger flow:

(1) Manual collection. Common manual data survey methods include site survey, vehicle follow-up survey, and vehicle survey. A large amount of labor is invested in questionnaire surveys, which can easily lead to uncontrollable survey data quality. Statistical data analysis is laborious and time-consuming, and survey costs are relatively high. High, and cannot continuously detect the spatio-temporal evolution of passenger flow.

(2) Intelligent equipment collection, combined with advanced sensing technology, face recognition technology, human body signal detection technology, real-time acquisition of records of each passenger getting on and off the bus, and using smart devices to statistically analyze the getting on and off of each station and the number of people in the car. Common collection methods include bus IC cards, RFID technology, pressure sensors, infrared and face recognition, etc. Each intelligent collection method has its own advantages and disadvantages. Although the bus IC card can accurately obtain passenger boarding and disembarking information from the perspective of technical implementation, generally passengers swipe their cards when they get on the bus and do not swipe their cards when they get off. RFID technology ignores the impact of electronic tags on the external environment of the bus, and its application requirements are very high. The bus passenger flow analysis based on the combination of face recognition and position positioning technology is a new technical means. It can not only collect the number of people getting on and off at the bus station, cross-sectional flow, but also analyze the distribution of bus OD according to the position information. .

To sum up, the detection system and method of bus passenger flow combined with face recognition and location positioning can provide a good decision-making basis for bus route planning, operation management, and route adjustment.

Utility model content

Aiming at the deficiencies of the existing bus passenger flow detection system and method, the utility model provides a new type of bus passenger flow detection system with high precision and real-time detection, which obtains the passengers' boarding and alighting time through the uniqueness of facial features Information and combined with location positioning information to obtain the location information of passengers getting on and off the bus, and through data mining and analysis, the number of people getting on and off at the bus station, cross-sectional flow, and bus OD and other data.

The purpose of this utility model is achieved by the following technical solutions: a bus passenger flow system based on face recognition and position positioning, including an image acquisition module connected in sequence, a bus position acquisition module, a data mining center and a data analysis center; On the one hand, the camera installed on the bus is used to collect the video information of passengers getting on and off the bus. The image preprocessing module preprocesses the image information and transmits the face image to the storage module of the data mining center. On the other hand, the location acquisition module is used to collect the bus The driving track information is sent to the storage module of the data mining center.

The image collection module is used to collect face images of getting on and off the bus; the bus position collection module is used to collect the running track of the bus; the data mining center includes a storage module, an image An analysis module and a position analysis module; the image analysis module and the position analysis module are connected with the storage module of the data analysis center; the storage module is used to store the face images of the people getting on and off the bus, information on getting on and off the bus, and the bus travel track; The image analysis module is used to analyze the collected images to determine the information of people getting on and off the bus corresponding to the image; the position analysis module matches the time of image capture with the recording time of the bus travel track to analyze the getting on and off and the position ; The data analysis center includes a traffic analysis module, which is connected to the storage module of the data mining center; the traffic analysis module is used for statistical analysis of single-point traffic and OD distribution of bus lines and stations.

The application of the utility model not only improves the detection accuracy of the bus passenger flow, but also improves the frequency of the bus passenger flow detection, and greatly saves the flow investigation cost.

Description of drawings

Below in conjunction with accompanying drawing and specific embodiment, the utility model is described in further detail.

Fig. 1 is the functional block diagram of the utility model system.

detailed description

The technical scheme of the utility model is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the utility model is not limited to the following description.

As shown in Figure 1, the bus passenger flow detection system based on face recognition and location location includes sequentially connected image acquisition module, bus location acquisition module, data mining center and data analysis center;

The image acquisition module is used to collect face images of getting on and off the bus; the bus position acquisition module is used to collect the driving trajectory of the bus;

The data mining center includes a storage module, an image analysis module and a position analysis module; the image analysis module, the position analysis module are connected with the storage module of the data analysis center;

The storage module is used to store face images of people getting on and off the bus, information on getting on and off the bus, and the trajectory of the bus;

The image analysis module is used to analyze the collected images, and determine the information of the personnel getting on and off the vehicle corresponding to the images;

The position analysis module matches the time of image capture and the recording time of the bus travel track, and analyzes the getting on and off and the position;

The data analysis center includes a flow analysis module connected with the storage module of the data mining center;

The flow analysis module is used for statistical analysis of single-point flow and OD distribution of bus lines and stations.

It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, no matter from all points of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, so it is intended to fall within the scope of the claims All changes within the meaning and range of equivalents of the required elements are included in the present invention. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (2)

1. bus passenger flow amount detection systems based on recognition of face and location positioning, it is characterised in that: include image capture module, position of bus acquisition module, data mining center and the data analysis center being sequentially connected with；Image capture module is acquired for the facial image getting on or off the bus bus, position of bus acquisition module is for being acquired the driving trace of bus, data mining center is for carrying out human face analysis and position analysis to the image collected and positional information, data analysis center carries out flow analysis for the result obtained according to data mining center analysis, provides decision-making foundation for public bus network planning, operational management, line adjustment etc.；Described image capture module is acquired for the facial image getting on or off the bus bus；Described position of bus acquisition module is for being acquired the driving trace of bus；Described data mining center includes memory module, image analysis module and location analysis module；Image analysis module, location analysis module are connected with the memory module of data analysis center；Described memory module is got on or off the bus the facial image of personnel, information of getting on or off the bus and bus driving trace for storing bus；Described image analysis module is for being analyzed the image collected, it is judged that the personnel that image is corresponding get on or off the bus information；Described location analysis module passes through image capturing time and bus travel track record time match, and analysis draws gets on or off the bus and position；Described data analysis center includes flow analysis module, is connected with the memory module at data mining center；Described flow analysis module is for statistical analysis public bus network, the single-point flow of website and OD distribution situation.

Bus passenger flow amount detection systems based on recognition of face and location positioning the most according to claim 1, it is characterized in that: described image capture module includes two photographic head of image pre-processing module and bus front/rear door, described image pre-processing module for camera collection to image carry out pretreatment, obtain facial image of the same size with image in memorizer.