Subway passenger travel space-time trajectory identification method and system based on pedestrian re-identification

Abstract

The invention provides a subway passenger travel space-time track identification method and system based on pedestrian re-identification, and the method comprises the following steps: matching card swiping data in an automatic fare collection system, associating the card swiping data, a monitoring video and a picture position, and achieving the binding of multiple types of information; acquiring a pedestrian sample feature library of monitoring videos of each card number passing through the entrance gate and the exit gate; searching K travel paths of the arrival-departure route; obtaining an effective alternative path set; calculating the earliest moment and the latest moment of the passenger appearing at each monitoring point; comparing the complete path set of each alternative path in the previous step to obtain a screened comparison path set and a monitoring video node set; calculating a monitoring node minimum set selection scheme meeting a preset requirement; and determining the path of the monitoring node with the maximum feature similarity as the actual travel path of the passenger. According to the method and the system, the accurate passenger flow distribution result can be efficiently obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of passenger flow monitoring, and specifically, relates to a method and system for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, and more specifically, to a method, system, electronic device and computer-readable storage medium for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification. Background Art

[0002] Large-scale network operation of urban rail transit is the new normal of urban development. Among them, 13 cities such as Shanghai, Beijing, Chengdu, and Guangzhou have an operating mileage of more than 300km. The continuous expansion of the network scale has led to a more complex road network topology, more travel paths between any two nodes in the network, and more random travel path selection behavior of passengers, which poses a huge challenge to the accurate calculation of the urban rail transit network clearing and settlement model. Every year, large cities such as Beijing and Shanghai need to invest a lot of manual surveys for parameter calibration and on-site verification of the clearing model to improve the calculation accuracy of the model. The core problem lies in how to accurately identify the travel time and space paths of passengers in a super-large-scale road network, so as to obtain accurate passenger flow distribution results. How to accurately identify the travel time and space paths of passengers in a super-large-scale road network, so as to obtain accurate passenger flow distribution results, is a bottleneck problem that needs to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, aiming to solve the technical problem of inaccurate identification of passengers' travel spatiotemporal paths in ultra-large-scale road networks in the prior art.

[0004] To achieve the above object, the technical solution adopted by the present invention is: to provide a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, comprising the following steps:

[0005] Match the card swiping data in the automatic ticket vending and checking system, associate the card swiping data, surveillance video and screen position, and realize the binding between multiple categories of information;

[0006] Collect the pedestrian sample feature library of each card number from the surveillance video at the entrance and exit gates;

[0007] Search for K travel paths of the inbound-outbound route; eliminate invalid paths to obtain a set of valid alternative paths;

[0008] Calculate the set of surveillance videos of any candidate route passing through each identification station; divide the itinerary of the candidate route into N segments with the transfer station as the dividing point; calculate the earliest and latest time when the passenger appears at each monitoring point;

[0009] Compare the complete path set of each candidate path in the previous step, remove the overlapping identification stations, and obtain the screened comparison path set and monitoring video node set;

[0010] Taking the video surveillance node set of each candidate path obtained by the above step after eliminating duplicate identification as input, a multi-point joint identification and detection method is proposed to calculate the minimum set of surveillance nodes that meets the preset requirements;

[0011] Compare the pedestrian feature similarities of the monitoring nodes in each valid alternative path in the previous step, and determine that the suspected target passenger with the greatest feature similarity is the correct value, and determine that the path where the monitoring node with the greatest feature similarity is located is the actual travel path of the passenger.

[0012] Preferably, the matching of the card swiping data in the automatic ticket vending and checking system, associating the card swiping data, the surveillance video and the screen position, and realizing the binding between multiple categories of information includes:

[0013] Read the card swiping data in the automatic ticket vending and checking system to obtain a data set containing entry and exit information;

[0014] Obtain all the entry gates, exit gates and surveillance video points of the station, mark the position of passengers passing through each gate in the surveillance video screen; realize the binding of gate information with surveillance video and screen position multiple sets of information.

[0015] Preferably, the pedestrian sample feature library for collecting each card number from the monitoring video at the entry gate and the exit gate includes:

[0016] Read the card number, entry gate, and entry time information of the card swiping data; find the surveillance video of the entry gate; collect the video image of the passenger passing the gate at the time of the entry gate in the surveillance video; sample human body features and mark them as the entry sample features of the card number;

[0017] Read the card number, exit gate, and exit gate time information of the card swiping data; find the surveillance video of the exit gate; collect the video image of the passenger passing the gate at the exit gate time in the surveillance video; perform human body feature sampling and mark it as the exit sample feature of the card number;

[0018] Identify the complete process of the passenger entering and exiting the station gate from entering the video screen to leaving the video screen; automatically collect all the video image features of the passenger as the sample feature material of the passenger;

[0019] Establish a training sample feature library for the historical travel of pedestrians corresponding to each card number.

[0020] Preferably, the search is for K travel paths of the entry-exit route; invalid paths are eliminated to obtain a set of valid alternative paths, including: when the minimum riding time of the path is greater than or equal to the actual travel time of the passenger and / or the average travel time of the path is greater than M times the average travel time of the shortest path of the OD route, wherein M≥1.5, the path is an invalid path.

[0021] Preferably, the step of calculating a set of surveillance videos of any candidate route passing through each identification station; dividing the trip of the candidate route into N trip segments with the transfer station as a dividing point; and calculating the earliest time and the latest time when the passenger appears at each monitoring point includes:

[0022] Based on the distribution map of video surveillance points in the station, a database table of video surveillance point sets of all the walking routes and paths of passengers from the starting point to the end point in the station is calculated; based on the starting point and end point of the alternative path at a certain identification station, the set of surveillance points included in the identification station is searched;

[0023] Taking the transfer station passed by the alternative route as the dividing point, the trip of the alternative route is divided into m trip segments and m-1 transfers; the trip segment range is from the boarding platform to the getting-off platform of the route passed by the alternative route, and the transfer starting point-end point is from the getting-off platform of the transfer station to the boarding platform of the next route;

[0024] Based on the walking time into the first stop, the exit time at the last stop, the fixed boarding time for each journey segment, the waiting time, and the walking time for transfers between different journey segments, the earliest and latest times that passengers appear at each video surveillance point are calculated.

[0025] Preferably, the earliest time and the latest time when the passenger appears at each video surveillance point are calculated based on the walking time for entering the first station, the exit time for the last station, the fixed boarding time for each journey segment, the waiting time, and the transfer walking time between different journey segments, including: The calculation formula for the whole travel time is:

[0026]

[0027] Where, t in is the walking time for passengers to enter the station at the starting station O; t out It is the walking time for passengers to exit the terminal station D; is the waiting time of passengers at the starting station of the i-th journey segment; is the fixed running time of the train in the i-th journey section; is the transfer walking time of the passenger for the i-th transfer.

[0028] Preferably, the earliest time and the latest time when the passenger appears at each video surveillance point are calculated based on the walking time for entering the first station, the exit time for the last station, the fixed boarding time for each journey segment, the waiting time, and the transfer walking time between different journey segments, including: the calculation formula for the fixed running time of the passenger in each route journey segment under the path r is:

[0029]

[0030] In the formula, is the fixed running time of the train in the i-th line segment; t j,stop is the train’s stop time at station j; tj→j+1,run is the travel time of the train from station j to station j+1; is and ie are the starting station and ending station numbers of the i-th journey segment respectively.

[0031] Preferably, the alternative path is divided into m travel segments and m-1 transfers by taking the transfer station as the dividing point; wherein the travel segment ranges from the boarding platform to the getting-off platform of the line passed by the alternative path, and the transfer starting point-end point is from the getting-off platform of the transfer station to the boarding platform of the next line, including: the complete travel path of the alternative path r Pr The expression is:

[0032] P r = {G in ,B r,1 ,(T r,1 ,B r,2 ), L(T r,i ,B r,i+1 )L(T r,m-1 ,B r,m ),G out},m≥1

[0033] T r,i =(S r,i.e ,S r,(i+1).s ),B r,i+1 =(S r,(i+1).s ,S r,(i+1).e ),

[0034] Among them, G in G is the entry gate of the starting station; out T is the exit gate of the terminal station; r,i is the i-th transfer in the itinerary of path r, from the alighting station S of the i-th itinerary segment r,i.e Go to the platform S for the i+1th segment r,(i+1) .s; B r,i+1 is the i+1th segment of path r, including the starting node S of the boarding platform r,(i+1).sTo the end node S of the platform where you get off r,(i+1).e .

[0035] Preferably, the video surveillance node set of each candidate path obtained by the above step after eliminating duplicate identification is used as input, and a selection scheme for calculating the minimum set of surveillance nodes that meets preset requirements based on a multi-point joint identification and detection method is proposed, including:

[0036] According to the passenger's walking path, the video surveillance nodes of a certain alternative path at each identification station are connected in parallel or in series;

[0037] The detection accuracy of the monitoring nodes is sorted from high to low, and the pedestrian re-identification detection algorithm is called to find the suspected target passenger in the historical video within the corresponding time range of the monitoring node, and the feature similarity with the target passenger is calculated.

[0038] The subway passenger travel space-time trajectory recognition system based on pedestrian re-identification is characterized in that it is used to execute the subway passenger travel space-time trajectory recognition method based on pedestrian re-identification as described in any one of the above items, including:

[0039] Data access processing module, used to match the card swiping data in the automatic ticket vending and checking system, associate the card swiping data, surveillance video and screen position, and realize the binding between multiple categories of information;

[0040] Passenger identity binding module, used to realize the identity binding of passenger card number and monitoring image features, and establish a historical image feature library for each passenger;

[0041] The feature sampling modeling module is used to collect video image features of the passengers to be compared, collect the appearance features, gait features, and motion trajectory features of the pedestrians in the surveillance videos at the entry and exit gates through which the passengers pass, and establish a deep learning training model for pedestrian re-identification;

[0042] The effective path search module is used to calculate K travel paths of any alternative path, and eliminate invalid paths based on the actual travel time of passengers to obtain effective alternative paths;

[0043] The monitoring point search module is used to search for the monitoring node set of the identification station passed by each alternative route, calculate the time range of passengers arriving at each monitoring node, and narrow the number and time range of videos to be compared;

[0044] The pedestrian re-identification module is used to search for the target passenger in the historical video data under the constraints of the specified monitoring node and time range, calculate the feature similarity and detection time of the suspected target passenger, and record the results;

[0045] The travel path identification module is used to call the pedestrian re-identification module for the monitoring node of each alternative path, compare the maximum value and frequency of feature similarity of each path, and determine the actual path of the passenger.

[0046] The method and system for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification provided by the present invention and the assembly method have the following beneficial effects: Compared with the prior art, the method and system for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification of the present invention establishes a passenger video image feature database for each ticket ID, and realizes the accurate matching of AFC card swiping data and video image data. Rapidly screen the effective alternative travel path set of passengers, focus on the path that needs to be compared, and eliminate some obviously invalid paths. Search for the surveillance cameras passing through each alternative path, find out the surveillance cameras used to distinguish different paths, and calculate the time range for the passenger to arrive at each camera. Reduce the number and time range of cameras to be compared, and reduce invalid searches. Propose a pedestrian re-identification comparison scheme based on multi-level camera reconnection to improve the recognition accuracy of the spatiotemporal trajectory path of passengers. Improve the hit accuracy of recognition. Propose a system device for passenger travel trajectory recognition, which automatically realizes the functions of feature sampling, model training, feature comparison, and result output. Realize the automation and intelligence of the recognition system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flowchart of a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification provided by an embodiment of the present invention;

[0049] Figure 2 This is the location map of the 020605 gate of XJK station (G5 position);

[0050] Figure 3 This is the surveillance video and location corresponding to the XJK station entrance gate 020605 (1st from left);

[0051] Figure 4 The surveillance video and location corresponding to JMS's exit gate 032202 (2nd from right);

[0052] Figure 5 The first of three alternative routes from XJK Station to JMS Station;

[0053] Figure 6 The second of the three alternative routes from XJK Station to JMS Station;

[0054] Figure 7 There are three alternative routes from XJK station to JMS station;

[0055] Figure 8 This is the distribution map of monitoring points of DXG station;

[0056] Fig. 9 Illustration of removing duplicate identification stations from two alternative paths Figure 1 ;

[0057] Fig.10 Illustration of eliminating duplicate identification stations for two alternative paths Figure 2 ;

[0058] Fig.11 A schematic diagram of a method for parallel and series cascade combination detection;

[0059] Fig.12 Schematic diagram of the two paths;

[0060] Fig.13 This is a schematic diagram of the escalator distribution on platform 0322 of the station;

[0061] Fig.14 This is a schematic diagram of the target passenger 080****5600. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0064] At present, subway stations in all cities across the country have achieved full coverage of cameras in key areas. Taking Beijing as an example, about 56,000 cameras are installed in 477 stations of Beijing Subway, and each station has more than 100 cameras, covering most of the key areas. It has become possible to track the movement trajectory of pedestrians based on cameras. Therefore, video-based pedestrian re-identification technology can theoretically solve the problem of pedestrian travel path identification. However, due to the large number of subway stations in the entire network (Beijing and Shanghai have more than 450 stations) and the large number of cameras in each station, the massive, large-scale, and long-term video spatiotemporal retrieval and comparison requires a large amount of transportation resources and long-term calculations, which has become a bottleneck problem for the application and promotion of this technology.

[0065] In conjunction with the accompanying drawings, the method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification provided by the embodiment of the present application is described in detail below through specific embodiments and their application scenarios.

[0066] Reference Figures 1 to 14 , shows a flowchart of a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification provided by an embodiment of the present invention. The method may specifically include the following steps:

[0067] Step S1: Match the card swiping data in the Automatic Fare Collection (AFC) system, associate the card swiping data, surveillance video and screen position, and realize the binding between multiple categories of information. Among them, the card swiping data includes entry information and exit information.

[0068] Step S1 may include the following sub-steps:

[0069] Sub-step S1.1, reading the AFC card swiping data of entering and exiting the station, and obtaining a data set including the entry information and the exit information.

[0070] The entry information is obtained by reading the entry gate data. The exit information is obtained by reading the exit gate data. Among them, the entry gate information includes the entry record, specifically, the entry record includes: card number, transaction date, entry station, entry time, entry gate. The exit gate information includes the exit record, specifically, the exit record includes: card number, transaction date, exit station, exit time, exit gate.

[0071] Sub-step S1.2, obtain all the station entry gate sets, exit gate sets and monitoring video points, mark the position of passengers passing through each gate in the monitoring video screen; realize the binding of gate information with monitoring video and screen position multiple sets of information.

[0072] The station gate group consists of multiple gates, each of which corresponds to a surveillance video. The position where the passenger appears on the surveillance video screen when swiping the card to pass through the gate is marked, and the gate code (including entry gate and exit gate), surveillance video point, and screen position information are bound together.

[0073] Step S2: Collect the pedestrian sample feature library of each card number in the monitoring video of the entry gate and the exit gate. Specifically, read each complete AFC card swiping data, and collect the pedestrian sample feature library of each card number in the monitoring video of the entry gate and the exit gate.

[0074] Step S1 ensures that when a passenger swipes a card to pass through the gate in this step, a pedestrian feature image corresponding to the monitoring video point, gate-passing time, and characteristic screen position can be accurately collected.

[0075] Step S2 may include the following sub-steps:

[0076] Sub-step S2.1, read the card number, entry gate, and entry time information of the card swiping data; find the surveillance video of the entry gate; collect the video image of the passenger passing through the gate at the time of entry; sample human body features and mark them as the entry sample features of the card number.

[0077] Sub-step S2.2, read the card number, exit gate, and exit gate time information of the card swiping data; find the surveillance video of the exit gate; collect the video image of the passenger passing the gate at the exit gate time in the surveillance video; sample human body features and mark them as the exit sample features of the card number.

[0078] Sub-step S2.3, based on the Yolo-v5 pedestrian detection algorithm and the Deep-sort tracking algorithm, identify the complete process of the passenger entering and exiting the station through the gate from entering the video screen to leaving the video screen; automatically collect all the video image features of the passenger as the sample feature material of the passenger.

[0079] Sub-step S2.4: Combine the historical travel records of each card number and establish a training sample feature library for the historical travel of the pedestrian corresponding to each card number.

[0080] If the type of the card number is not a one-way ticket, the historical travel records of the card number can be retrieved, and the sample features collected historically can be added to the sample feature library of the day for model training.

[0081] The sample features of each pedestrian include pedestrian appearance features, gait features, motion trajectory features, etc.

[0082] In one example, referring to Figure 2and Table 1, wherein Table 1 is the complete AFC card swiping data of a certain city subway line network from XJK station (0206) to JMS station (0322); Figure 2 The location map of gate 020605 at XJK station is shown.

[0083] Table 1 Complete AFC record from XJK to JMS

[0084]

[0085] Read the first record, determine that the entry station is 0206, the entry gate is 020605, and find the location distribution map corresponding to the gate (refer to Figure 2 ) and find the corresponding video screen of the entry gate (refer to Figure 3 ).

[0086] The surveillance video of the passenger with card number 080****5600 at XJK station gate 020605 (refer to Figure 3 ). When there is a time deviation between the clock of the AFC system and the clock of the video surveillance system, a time deviation correction operation is performed. After locating the pedestrian, the pedestrian detection and tracking algorithm can be used to calculate the complete video clip of the pedestrian 080****5600 passing through the gate as a training model sample.

[0087] The surveillance video of the passenger with card number 080****5600 at the exit gate 032202 of JMS (refer to Figure 4 ). After locating the pedestrian, the pedestrian detection and tracking algorithm is used to calculate the complete video clip of the pedestrian 080****5600 passing through the gate as a training model sample.

[0088] Step S3: With the complete AFC card swiping data from step S2, search for K travel paths corresponding to the OD route according to the information of the entry station O and the exit station D. Calculate the average travel time, en route travel time and the actual travel time of the passenger for each path, eliminate invalid paths, and obtain a set of valid alternative paths.

[0089] If any of the following conditions are met in the path searched in step S3, it is considered as an invalid path for the passenger:

[0090] ①The minimum travel time of the route is greater than or equal to the actual travel time of the passenger;

[0091] ②The average travel time of this path is >M times the average travel time of the shortest path of the OD route, where M≥1.5.

[0092] It should be noted that in this step, the minimum travel time for each route does not include the waiting time for each journey segment, that is, it is composed of: the minimum walking time for entering the starting station, the fixed train running time for each journey segment, the minimum transfer walking time when passing through the transfer station, and the minimum exit walking time at the terminal station.

[0093] The fixed running time of the train for each journey segment is composed of the running time of the section passed by the journey segment + the stop time at the station passed by.

[0094] The actual travel time of the passenger = exit gate time - entry gate time.

[0095] The average travel time of this route is composed of: the average walking time into the starting station, the fixed train running time of each journey segment, the average waiting time for each journey segment, the average transfer walking time at the transfer station, and the average exit walking time at the terminal station.

[0096] In an example, a simple subway network consists of four lines. There are three possible routes from XJK station to JMS station, which are Figure 5 , Figure 6 , Figure 7 The calculation results of the in-transit travel time and average travel time of the three routes from XJK Station to JMS Station are shown in Table 2: According to the entry and exit times of passenger 080****5600, the actual travel time is calculated to be 1108 seconds. According to the first judgment rule of invalid paths, all three paths meet the requirements; according to the second judgment rule, M=1.5, the shortest path travel time*1.5=1335 seconds, and the third path 1785>1335, which is judged as an invalid path. Therefore, after eliminating path 3, the valid paths are path 1 and path 2.

[0097] Table 2 Travel route composition and travel time from XJK station to JMS station

[0098]

[0099] Step S4: Based on the distribution of surveillance video points at the station, calculate the surveillance video set of a certain alternative route passing through each identification station; divide the itinerary of the alternative route into N itinerary segments with the transfer station as the dividing point. Based on the walking time for entering the first station, the walking time for leaving the last station, the fixed boarding time for each itinerary segment, and the transfer walking time between different itinerary segments, calculate the earliest and latest time that the passenger appears at each monitoring point.

[0100] Step S4 may include the following sub-steps:

[0101] Sub-step S4.1: Based on the distribution map of video surveillance points in the station, calculate the database table of video surveillance point sets of all the walking routes and paths of passengers from the starting point to the end point in the station. Based on the starting point and end point of the alternative path at a certain identification station, search for the set of surveillance points included in the identification station.

[0102] In one example, Table 3 shows a monitoring point query table of the DXG station. Figure 8 The distribution diagram of the monitoring points of DXG station is shown in Table 3 and Figure 8 You can query the collection and distribution of monitoring points that pedestrians pass through from different starting locations to the end locations.

[0103] Table 3 Monitoring point query table from different starting positions to end positions of DXG station

[0104]

[0105] Sub-step S4.2: Taking the transfer station of the alternative route as the dividing point, the trip of the alternative route is divided into m trip segments and m-1 transfers (m≥1). The trip segment ranges from the boarding platform to the getting-off platform of the route of the alternative route, and the transfer starting point-end point is from the getting-off platform of the transfer station to the boarding platform of the next route.

[0106] In this step, the complete travel path of alternative path r Pr The expression is:

[0107] P r = {G in ,B r,1 ,(T r,1 ,B r,2 ), L(T r,i ,B r,i+1 )L(T r,m-1 ,B r,m ),G out},m≥1

[0108] T r,i =(S r,i.e ,S r,(i+1).s ),B r,i+1 =(S r,(i+1).s ,S r,(i+1).e ),

[0109] Among them, G in G is the entry gate of the starting station; out T is the exit gate of the terminal station; r,i is the i-th transfer in the itinerary of path r, from the alighting station S of the i-th itinerary segment r,i.e Go to the platform S for the i+1th segment r,(i+1).s ; B r,i+1is the i+1th segment of path r, including the starting node S of the boarding platform r,(i+1).s To the end node S of the platform where you get off r,(i+1).e

[0110] The complete surveillance video set C of the candidate path r at all recognition stations r , the set of monitoring points that the path passes through on the walking route into the station, the bus route, the transfer walking route, and the walking route out of the station is C r,in , C r,B , C r,T , C r,out The union of; the solution expression is:

[0111] C r =σ in C r,in Uσ B C r,B Uσ T C r,T Uσ out C r,out

[0112] where

[0113] C r,in =f(G in ,S r,1.s ),

[0114] C r,out =f(S r,m.e ,G out ),

[0115]

[0116] Where: C r,in is the camera set on the walking route into the station, f(G in ,S r,1.s,1 ) for passengers entering the station from gate G in Walk to the starting node S of the first route segment r,1.s,1 A collection of surveillance cameras along the way; C r,out is the camera set on the walking route out of the station, f(S r,m.e,n ,G out ) represents the passenger's final node S from trip segment m r,m.e,n Walk to exit gate G out A collection of surveillance cameras passed by; C r,B is the camera set on the bus route, f(S r,i.s, S r,i.e ) indicates that the passenger starts from the boarding station of the i-th route segment. r,i.s To the end node S of the next station platform r,i.eA collection of surveillance cameras passing by, To find the union of m route segments. r,T is the camera set on the transfer route, f(S r,i.e ,S r,(i+1).s ) represents the end node S of the passenger’s alighting platform at the i-th line segment r,i.e Transfer to the starting node S of the boarding platform of the i+1th line segment r,(i+1).s A collection of surveillance cameras passing by, To find the union of m-1 route segments. in , σ B , σ T , σ out It is a Boolean variable that determines whether the camera set is selected.

[0117] Further, the function f(x, y) is used to calculate the set of monitoring points that the passenger passes through from the starting node x to the ending node y. This can be obtained by using the table lookup method in sub-step S4.1.

[0118] Further, the priority of selecting the camera set is C r,T >C r,in =C r,out >C r,B ; σ T The default value is 1, B , σ T , σ out The default value is 0; if the first station is a transfer station, σ in =1; if the last station is a transfer station, then σ out =1; if the alternative path contains a loop, to distinguish the different directions of the loop, then σ B =1.

[0119] Sub-step S4.3, based on the walking time for entering the first station, the exit time for the last station, the fixed boarding time for each journey segment, the waiting time, and the transfer walking time between different journey segments, calculate the earliest time and the latest time that the passenger appears at each video surveillance point.

[0120] Step S4.3 may include the following sub-steps:

[0121] Sub-step S4.3.1, for a certain OD pair, the total travel time of the alternative route r is composed of the walking time for entering the station, the waiting time for the journey segments passing through all lines, the fixed running time, the transfer walking time for the transfer journey segments, and the walking time for leaving the station, as shown in the following formula:

[0122]

[0123] Where, t in is the walking time for passengers to enter the station at the starting station O; tout It is the walking time for passengers to exit the terminal station D; is the waiting time of passengers at the starting station of the i-th journey segment; is the fixed running time of the train in the i-th journey section; is the transfer walking time of the passenger for the i-th transfer; m is the number of journey segments.

[0124] Furthermore, the fixed running time of each line trip segment of the passenger under the path r is the sum of the stop time of all stations in the running direction of the trip segment and the running time of the forward section;

[0125]

[0126] In the formula, is the fixed running time of the train in the i-th line segment; t j,stop is the train’s stop time at station j; tj→j+1,run is the travel time of the train from station j to station j+1; is and ie are the starting station and ending station numbers of the i-th journey segment respectively.

[0127] Sub-step S4.3.2, based on the entry gate time, forwardly calculate the earliest time when the passenger appears at the monitoring point j of each identification station k, and based on the exit gate time, reversely calculate the latest time when the passenger appears at the monitoring point j of each identification station k.

[0128] The implementation process of this step can be as follows: First, the earliest time when the passenger appears at the monitoring point j of the identification station k is calculated as:

[0129]

[0130] Where: T in The time when passengers enter the station; is the cumulative travel time of the passenger in the first k-1 segments; if k-1≤0, this item is 0; is the cumulative transfer walking time of the passenger at the first k-2 identification stations. The identification stations include: the first station, the transfer stations along the way, and the last station; if k-2≤0, then this item is 0; t in,min is the minimum walking time for passengers to enter the first station; σ1 is a Boolean variable; g(p k.s ,p k.j ) is the passenger’s starting position p from the identification station k k.s To monitoring point j position p k.j Minimum walking time;

[0131] Then, the latest time for a passenger to appear at the identification station in the i-th trip segment is calculated as:

[0132]

[0133] Where: T out The time it takes for passengers to exit the station; is the cumulative travel time of the passenger in the last mk segments; if mk≤0, this item is 0; is the cumulative transfer walking time of the passenger at the next mk-1 transfer stations. If mk-1≤0, this item is 0; t out,min is the minimum walking time for passengers to enter the last stop; σ2 is a Boolean variable; g( pk.j ,p k.e) The passenger moves from monitoring point j to position p k.j To the end position p of the identification station k k.e Minimum walking time;

[0134] Furthermore, the time range of a passenger’s appearance at monitoring point j of each identification station k is [T kj,ET ,T kj,LT ], Search duration:

[0135] t kj,find =T kj,LT -T kj,ET

[0136] Furthermore, the acceleration ratio of the actual travel time and the search time of the passenger calculated based on this method is calculated:

[0137]

[0138] From the above formula, we can see that the larger the ratio is, the more time is saved and the higher the calculation efficiency is.

[0139] The use of this step can effectively solve the problems of the search range and search efficiency of surveillance cameras and the problem of accurately estimating the search time range for cross-lens pedestrian re-identification.

[0140] In one example, the calculation results of the travel segment division of the two paths, the identification station and the walking route, and the search time range are shown in Table 4 and Table 5:

[0141] The passenger's entire journey took 1108 seconds, and the search time for each node on path 1 and path 2 was 418 seconds and 408 seconds, respectively. The calculated acceleration ratios were 2.65 times and 2.72 times, which significantly improved the efficiency of time search.

[0142] Table 4 Search time range for each trip segment of route 1 from XJK station to JMS station

[0143]

[0144] Table 5 Search time range for each trip segment of route 2 from XJK station to JMS station

[0145]

[0146]

[0147] Step S5: Compare the complete path set of each candidate path in the previous step, remove the overlapping identification stations, and obtain a screened comparison path set and monitoring video node set.

[0148] In one example, referring to Figures 9 and 10 , taking 2 paths as an example:

[0149] Path 1: 0101G1-0101DN-0102DN / 0408UP-0404UP / 0302DN-0306DN-0306G2

[0150] Path 2: 0101G1-0101DN-0105DN / 0208UP-0204UP / 0305DN-0306DN-0306G2

[0151] Note: DN stands for downlink platform, UP stands for uplink platform

[0152] Since the starting platform 0101DN and the terminal platform 0306DN of Path 1 and Path 2 are the same, the surveillance video collection of passengers appearing at the first and last stations can be eliminated.

[0153] The paths of Path 1 and Path 2 after elimination are actually the transfer nodes in the middle of the comparison, as shown below:

[0154] Identification station for path 1: [0102DN / 0408UP], [0404UP / 0302DN]

[0155] Identification stations for path 2: [0105DN / 0208UP], [0204UP / 0305DN]

[0156] Therefore, the surveillance video node set of path 1 and path 2 after elimination is:

[0157]

[0158]

[0159] Step S6, the video surveillance node set of each candidate path after eliminating duplicate identification calculated in the above step is used as input. In order to improve the accuracy of pedestrian re-identification on the path, a multi-point joint identification and detection method is proposed to calculate the minimum set of monitoring nodes that meets the minimum error.

[0160] Step S6 may include the following sub-steps:

[0161] Sub-step S6.1: According to the passenger's walking path, the video surveillance nodes of a certain alternative path at each identification station are connected in parallel or series; the parallel-connected surveillance nodes are regarded as a combined node, and its pedestrian re-identification detection accuracy is the product of the detection accuracy of the sub-monitoring nodes.

[0162] Furthermore, if the detection accuracy γ of the monitoring point is an evaluation of the pedestrian re-identification accuracy of the surveillance camera based on the prior sample, γ∈[0,1]. For the parallel structure, the combined detection accuracy is the product of the re-identification accuracy of all cameras at the monitoring node; the expression is as follows:

[0163]

[0164] Where: A r,j,P is the detection accuracy of path r in parallel at the jth monitoring node; γ j,k is the prior detection accuracy of the kth camera under the jth monitoring node, and n is the number of cameras at the video monitoring point.

[0165] For the combined monitoring accuracy of the series structure = 1-the product of all camera re-identification errors under the monitoring node, the expression is:

[0166]

[0167] Where: A r,j,S is the detection accuracy of path r in series with the jth monitoring node.

[0168] Furthermore, the parallel relationship can be physically interpreted as that at a certain video monitoring point, since the width of the detection area is too large, a single surveillance camera cannot cover it, and two or more cameras are required to detect side by side.

[0169] Sub-step S6.2, sort the detection accuracy of the monitoring nodes from large to small, call the pedestrian re-identification detection algorithm, search for suspected target passengers in the historical video within the corresponding time range of the monitoring node (obtained by step S4.3.2), and calculate the feature similarity with the target passenger. If the similarity exceeds the set threshold, it is determined that the target passenger is found, and records such as the monitoring point location, similarity value and appearance time are recorded.

[0170] The detection process of the pedestrian re-identification algorithm includes the following steps:

[0171] 1. In step S2, the pedestrian sample features of the pedestrian i whose card number is to be detected in the entrance gate and exit gate monitoring video are obtained as the sample features of the target to be found, and the vector is represented as F i ;

[0172] 2. For the surveillance video to be compared, the earliest and latest times of the pedestrian i calculated in step S4 are used as the search range, and the YOLO-V5 algorithm is used for multi-target detection, and Deep-sort is used for target tracking. The features corresponding to all pedestrian IDs in this period are calculated and output, and the vector is represented as F j , j represents the jth pedestrian in this period.

[0173] 3. Calculate the similarity between the features of the target pedestrian i and all the features of the pedestrian targets j to be compared. The similarity is determined by Euclidean distance. The formula is as follows, and the feature similarities are sorted from large to small.

[0174]

[0175] x i,s 、x j,s is the s-th sub-feature of feature pedestrian i, j, and n is the number of features of pedestrian i.

[0176] 4. Take the pedestrian target with the largest feature similarity and determine whether its similarity exceeds the set threshold. If so, it is determined that the target to be detected has been found.

[0177] To reduce invalid re-identification detections while meeting the expected re-identification accuracy target, a minimum error threshold is set. ε , calculate the minimum number of monitoring nodes g that need to be compared to satisfy the combined detection error of the first g series nodes E(g) Less than threshold ε , the formula is as follows:

[0178]

[0179] In the formula, Represents the product value of the identification and detection errors of the first g monitoring points.

[0180] In one example, referring to Fig.11 , assuming that the re-identification detection accuracy of c1, c2, and c3 is 0.9, the re-identification detection accuracy of c4 and c5 is 0.8, and the re-identification detection accuracy of c7, c8, and c9 is 0.9; then:

[0181] The cameras c1, c2, and c3 of the monitoring node M1 are in parallel, and λ1=1. Then the detection accuracy is:

[0182]

[0183] The cameras c4 and c5 of the monitoring node M2 ​​are connected in series, λ2=0, then the detection accuracy at this point is:

[0184]

[0185] Here, monitoring node M2 ​​is selected first for re-identification, and then monitoring node M1 is considered:

[0186] Set the minimum convergence error ε =0.01, then by connecting M1 and M2 in series, the combined detection error is

[0187] E(m)=(1-0.729)×(1-0.96)≈0.01, the convergence condition is met, m=2, then stop looking for the subsequent M3 monitoring node.

[0188] Step S7: compare the pedestrian feature similarities of the monitoring nodes in each valid candidate path in the previous step, and determine that the suspected target passenger with the greatest feature similarity is the correct value, and determine that the path where the monitoring node with the greatest feature similarity is located is the actual travel path of the passenger.

[0189] For example, the two paths are shown as Fig.12 On the walking route from the up platform of station 0322 to the exit gate 032202 on route 1, the surveillance camera on the west escalator (as shown Fig.13 As shown in Figure 2, we find the target pedestrian whose feature similarity is the largest. The actual image of the target is found as Fig.14 As shown, it can be inferred that the actual travel path of the passenger is path 1.

[0190] The present invention provides a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, which innovatively integrates AFC card swiping data and train operation diagram data and is based on ReID technology. The following problems are solved: 1. How to realize the identity binding of passenger ticket ID and pedestrian image features and automatic sampling of features; 2. How to scientifically select the monitoring points to be compared and the retrieval time range to improve the efficiency of image comparison and retrieval; 3. How to solve the problem of low re-identification accuracy of a single monitoring video through cascade recombination methods; 4. How to identify the travel path of passengers based on pedestrian re-identification results.

[0191] The present invention provides a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, establishes a passenger video image feature database for each ticket ID, and realizes accurate matching of AFC card swiping data and video image data. Rapidly screen a set of effective alternative travel paths for passengers, focus on the paths that need to be compared, and eliminate some obviously invalid paths. Search for surveillance cameras passing through each alternative path, find out the surveillance cameras used to distinguish different paths, and calculate the time range for passengers to arrive at each camera. Reduce the number and time range of cameras to be compared, and reduce invalid searches. A pedestrian re-identification and comparison scheme based on multi-level camera reconnection is proposed to improve the recognition accuracy of the spatiotemporal trajectory path of passengers. Improve the hit accuracy of recognition. A system device for passenger travel trajectory recognition is proposed to automatically realize the functions of feature sampling, model training, feature comparison, and result output. The automation and intelligence of the recognition system are realized.

[0192] The present invention also provides a system for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification, which is used to execute the steps in a method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as described in any of the above items, including: a data access processing module, a passenger identity binding module, a feature sampling modeling module, an effective path search module, a monitoring point search module, a pedestrian re-identification module, and a travel path identification module. The data access processing module is used to match the card swiping data in the automatic ticket vending and checking system, associate the card swiping data, monitoring video and screen position, and realize the binding between multiple categories of information; the passenger identity binding module is used to realize the identity binding of the passenger card number and the monitoring image feature, and establish a historical image feature library for each passenger; the feature sampling modeling module is used to collect the video image features of the passengers to be compared, realize the collection of the pedestrian appearance features, gait features, and motion trajectory features of the monitoring video at the entry gate and exit gate passed by the passengers, and establish a deep learning training model for pedestrian re-identification; the effective path search module is used to calculate K travel paths for any alternative path, and combine them. The actual travel time of passengers is combined, invalid paths are eliminated, and valid alternative paths are obtained; the monitoring point search module is used to search for the set of monitoring nodes of the identification stations passed by each alternative path, calculate the time range of the passengers arriving at each monitoring node, and narrow the number and time range of videos to be compared; the pedestrian re-identification module is used to search for target passengers in historical video data under the constraints of specified monitoring nodes and time range, calculate the feature similarity and detection time of suspected target passengers, and record the results; the travel path identification module is used to call the pedestrian re-identification module for the monitoring nodes of each alternative path, compare the maximum value and frequency of feature similarity of each path, and determine the actual path where the passenger is located.

[0193] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0194] An embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each process of the above-mentioned embodiment of the method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0195] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned embodiment of the method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0196] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0197] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0198] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0201] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0202] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0203] The above is a detailed introduction to the subway passenger travel spatiotemporal trajectory identification method, system, electronic device and computer-readable storage medium based on pedestrian re-identification provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying the spatiotemporal trajectories of subway passengers based on pedestrian re-identification, characterized in that: The following steps are involved: Match the card swiping data in the automatic ticket vending and checking system, associate the card swiping data, surveillance video and screen position, and realize the binding between multiple categories of information; Collect the pedestrian sample feature library of each card number from the surveillance video at the entrance and exit gates; Search for K travel paths for the inbound-outbound route; Eliminate invalid paths and obtain a set of valid alternative paths; Calculate the surveillance video set of any alternative path passing through each identification station; The itinerary of the alternative route is divided into N itinerary segments by taking the transfer station as the dividing point; Calculate the earliest and latest time that a passenger appears at each monitoring point; Compare the complete path set of each candidate path in the previous step, remove the overlapping identification stations, and obtain the screened comparison path set and monitoring video node set; Taking the video surveillance node set of each candidate path obtained by the above step after eliminating duplicate identification as input, a multi-point joint identification and detection method is proposed to calculate the minimum set of surveillance nodes that meets the preset requirements; Compare the pedestrian feature similarities of the monitoring nodes in each valid alternative path in the previous step, and determine that the suspected target passenger with the greatest feature similarity is the correct value, and determine that the path where the monitoring node with the greatest feature similarity is located is the actual travel path of the passenger.

2. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 1, characterized in that: The matching of the card swiping data in the automatic ticket vending and checking system, associating the card swiping data, the surveillance video and the screen position, and realizing the binding between multiple categories of information includes: Read the card swiping data in the automatic ticket vending and checking system to obtain a data set containing entry and exit information; Obtain all the entry gates, exit gates and surveillance video points of the station, mark the position of passengers passing through each gate in the surveillance video screen; realize the binding of gate information with surveillance video and screen position multiple sets of information.

3. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 2, characterized in that: The pedestrian sample feature library collected from the surveillance video of each card number passing through the entry gate and the exit gate includes: Read the card number, entry gate, and entry time information of the card swiping data; find the surveillance video of the entry gate; collect the video image of the passenger passing the gate at the time of the entry gate in the surveillance video; sample human body features and mark them as the entry sample features of the card number; Read the card number, exit gate, and exit gate time information of the card swiping data; find the surveillance video of the exit gate; collect the video image of the passenger passing the gate at the exit gate time in the surveillance video; perform human body feature sampling and mark it as the exit sample feature of the card number; Identify the complete process of the passenger entering and exiting the station gate from entering the video screen to leaving the video screen; automatically collect all the video image features of the passenger as the sample feature material of the passenger; Establish a training sample feature library for the historical travel of pedestrians corresponding to each card number.

4. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 3, characterized in that: The search is for K travel paths for the inbound-outbound route; Eliminate invalid paths and obtain a set of valid alternative paths, including: when the minimum travel time of the path is greater than or equal to the actual travel time of the passenger and / or the average travel time of the path is greater than M times the average travel time of the shortest path of the route, where M≥1.5, the path is an invalid path.

5. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 3, characterized in that: The calculation of any candidate path passing through a set of surveillance videos of each identification station; The itinerary of the alternative route is divided into N itinerary segments by taking the transfer station as the dividing point; Calculate the earliest and latest time that a passenger appears at each monitoring point, including: Based on the distribution map of video surveillance points in the station, a database table of video surveillance point sets of all the walking routes and paths of passengers from the starting point to the end point in the station is calculated; based on the starting point and end point of the alternative path at a certain identification station, the set of surveillance points included in the identification station is searched; Taking the transfer station passed by the alternative route as the dividing point, the trip of the alternative route is divided into m trip segments and m-1 transfers; the trip segment range is from the boarding platform to the getting-off platform of the route passed by the alternative route, and the transfer starting point-end point is from the getting-off platform of the transfer station to the boarding platform of the next route; Based on the walking time into the first stop, the exit time at the last stop, the fixed boarding time for each journey segment, the waiting time, and the walking time for transfers between different journey segments, the earliest and latest times that passengers appear at each video surveillance point are calculated.

6. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 5, characterized in that: The calculation formula for the entire travel time is as follows: Where, t in is the walking time for passengers to enter the station at the starting station O; t out It is the walking time for passengers to exit the terminal station D; is the waiting time of passengers at the starting station of the i-th journey segment; is the fixed running time of the train in the i-th journey section; is the transfer walking time of the passenger for the i-th transfer.

7. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 6, characterized in that: The calculation formula for calculating the earliest and latest time when a passenger appears at each video surveillance point is based on the walking time for entering the first station, the exit time for the last station, the fixed boarding time for each journey segment, the waiting time, and the transfer walking time between different journey segments, including: the calculation formula for the fixed running time of the passenger in each route journey segment under path r is: In the formula, is the fixed running time of the train in the i-th line segment; t j,stop is the train’s stop time at station j; t j→j+1,run is the travel time of the train from station j to station j+1; is and ie are the starting station and ending station numbers of the i-th journey segment respectively.

8. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 5, characterized in that: The alternative path is divided into m travel segments and m-1 transfers by taking the transfer station as the dividing point; wherein the travel segment ranges from the boarding platform to the getting-off platform of the line through which the alternative path passes, and the transfer starting point-end point is from the getting-off platform of the transfer station to the boarding platform of the next line, including: the complete travel path P of the alternative path r r The expression is: P r ={G in ,B r,1 ,(T r,1 ,B r,2 ),L(T r,i ,B r,i+1 )L(T r,m-1 ,B r,m ),G out },m≥1 T r,i =(S r,i.e ,S r,(i+1).s ),B r,i+1 =(S r,(i+1).s ,S r,(i+1).e ), Among them, G in G is the entry gate of the starting station; out T is the exit gate of the terminal station; r,i is the i-th transfer in the itinerary of path r, from the alighting station S of the i-th itinerary segment r,i.e Go to the platform S for the i+1th segment r,(i+1).s ; B r,i+1 is the i+1th segment of path r, including the starting node S of the boarding platform r,(i+1).s To the end node S of the platform where you get off r,(i+1).e .

9. The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification as claimed in claim 5, characterized in that: The above step calculates the set of video surveillance nodes of each candidate path after removing duplicate identification as input, and proposes a scheme for selecting the minimum set of surveillance nodes that meets the preset requirements based on a multi-point joint identification and detection method, including: According to the passenger's walking path, the video surveillance nodes of a certain alternative path at each identification station are connected in parallel or in series; The detection accuracy of the monitoring nodes is sorted from high to low, and the pedestrian re-identification detection algorithm is called to find the suspected target passenger in the historical video within the corresponding time range of the monitoring node, and the feature similarity with the target passenger is calculated.

10. A subway passenger travel spatiotemporal trajectory recognition system based on pedestrian re-identification, characterized in that: The method for identifying the spatiotemporal trajectory of subway passengers based on pedestrian re-identification according to any one of claims 1 to 9 comprises: Data access processing module, used to match the card swiping data in the automatic ticket vending and checking system, associate the card swiping data, surveillance video and screen position, and realize the binding between multiple categories of information; Passenger identity binding module, used to realize the identity binding of passenger card number and monitoring image features, and establish a historical image feature library for each passenger; The feature sampling modeling module is used to collect video image features of the passengers to be compared, collect the appearance features, gait features, and motion trajectory features of the pedestrians in the surveillance videos at the entry and exit gates through which the passengers pass, and establish a deep learning training model for pedestrian re-identification; The effective path search module is used to calculate K travel paths of any alternative path, and eliminate invalid paths based on the actual travel time of passengers to obtain effective alternative paths; The monitoring point search module is used to search for the monitoring node set of the identification station passed by each alternative route, calculate the time range of passengers arriving at each monitoring node, and narrow the number and time range of videos to be compared; The pedestrian re-identification module is used to search for the target passenger in the historical video data under the constraints of the specified monitoring node and time range, calculate the feature similarity and detection time of the suspected target passenger, and record the results; The travel path identification module is used to call the pedestrian re-identification module for the monitoring node of each alternative path, compare the maximum value and frequency of feature similarity of each path, and determine the actual path of the passenger.

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