Patent Publication Number: US-11380102-B2

Title: Method for video surveillance of the crossing of a line by people, associated computer program and device

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of French Patent Application No. 1908293, filed Jul. 22, 2019, the disclosure of which is herein incorporated by reference in its entirety. 
     The present invention relates to a method for the video surveillance of the crossing of a line by people, as well as associated computer program and device. 
     The invention applies for example to the identification of participants in a sporting activity, such as a foot or cycling race. 
     The international PCT application published under number WO 2010/075430 A1 describes a method for person identification in a video, and more specifically in an image of this video. More specifically, document WO 2010/075430 A1 proposes to use the visual signature when it is not possible to recognize a number worn by the person. Thus, it is proposed to compare the visual signature of the current image with the visual signature of another image of the person, in which an identification number worn by the person has been previously recognized. 
     Moreover, US patent application published under the number US 2018/0107877 A1 describes a method for person identification in a plurality of images. More specifically, document US 2018/0107877 A1 proposes to use the visual signature when it is not possible, in some of the plurality of images, to recognize a number worn by the person. Thus, it is proposed to compare the visual signature of each of these images with the visual signature of another image of the plurality, in which an identification number worn by the person has been recognized. 
     However, none of these documents proposes a solution for the video surveillance of the crossing of a line by people. 
     It may thus be desirable to provide a method for the video surveillance of the crossing of a line by people. 
     Therefore the object of the invention is a method for the video surveillance of the crossing of a line by people, characterized in that it comprises:
         for each of a plurality of successive images of a video stream from a camera pointing towards the line, the search for at least one person present in the image and the definition, for each person found, of a zone in the image, known as a person zone, surrounding this person at least partially;   for each of at least one person found, the grouping together into one tracklet of several person zones from successive images and surrounding this same person at least partially;       

     for each tracklet:
         the identification of the person in the tracklet based on person zones in the tracklet;   the determination of a moment at which the line is crossed by the person identified from the person zones in the tracklet; and   the addition of the name found and of the moment of crossing determined in at least some of the images containing the person zones in the tracklet.       

     Thus, thanks to the invention, the same person zones are used both to determine the moment at which the line is crossed, as well as the identity of the person crossing it. This synergy makes it possible to simplify the calculations and to make them quicker, which facilitates real-time video surveillance. 
     Optionally, identification of the person comprises:
         for each person zone in the tracklet, the determination of a visual signature of the person, known as local visual signature;   the determination of an aggregate visual signature of the person from local visual signatures;   the search for an identification number in the person zones;   if the identification number is not found, the search, from identification numbers each associated with one or more reference visual signatures, for that for which the visual signature or signatures associated has the smallest distance with the aggregate visual signature; and   if the identification number is found, the association of the aggregate visual signature with this identification number, in order to become a reference visual signature.       

     Also optionally, the aggregate visual signature is an average of the local visual signatures. 
     Also optionally, the method further comprises, for each determination of a local visual signature, the assessment of the reliability of this local visual signature and the aggregate visual signature is determined from, in addition to local visual signatures, their associated reliabilities. 
     Also optionally, the distance of the aggregate visual signature to several reference visual signatures is an average of the respective distances between the aggregate visual signature and the reference visual signatures. 
     Also optionally, searching for the identification number comprises:
         for each person zone in the tracklet, the search for at least one number present in the person zone and the definition, in the person zone, for each number found, of a zone, known as a number zone, surrounding this number;   for each number zone in the tracklet, recognition of the number present in the number zone and, for each number recognized, the assessment of the reliability of the recognition;   selecting one of the numbers recognized from the reliabilities of these recognized numbers; and   searching for the selected number among predefined identification numbers.       

     Also optionally, the selection of one of the recognized numbers from the reliabilities associated with these numbers comprises:
         filtering each number for which the reliability is below a predefined threshold; and   selecting one of the other numbers, known as reliable numbers, from their associated reliabilities.       

     Also optionally, the determination of the moment of crossing comprises:
         the determination, from the person zones in the tracklet, of that zone first crossing, in a predefined direction, a line with a fixed and predefined position in the images; and   the determination of a moment at which the line is crossed from a moment associated with the image containing the person zone crossing the line first.       

     A computer program, downloadable from a communication network and/or recorded on a medium readable by a computer and/or executable by a processor, is also proposed, characterized in that it comprises instructions for executing the steps of a method according to the invention, when said program is executed on a computer. 
     A device for the video surveillance of the crossing of a line by persons is also proposed, characterized in that it comprises:
         means designed, for each of a plurality of successive images of a video stream from a camera pointing towards the line, to search for at least one person present in the image and to define, for each person found, a zone of the image, known as a person zone, surrounding this person at least partially;   means designed, for each of at least one person found, to group together into one tracklet several person zones from successive images and surrounding this same person at least partially;   means designed, for each tracklet, to identify the person in the tracklet based on person zones in the tracklet;   means designed, for each tracklet, to determine a moment at which the line is crossed by the person identified from the person zones in the tracklet; and   means designed, for each tracklet, to add the name found and the moment of crossing determined in at least some of the images containing the person zones in the tracklet.       

     The invention will be better understood by means of the following description, given only as an example and made with reference to the attached drawings wherein: 
    
    
     
         FIG. 1  schematically represents the general structure of a foot race infrastructure in which the invention is implemented, 
         FIG. 2  schematically represents the general structure of a device for person identification in the infrastructure of  FIG. 1 , 
         FIG. 3  shows the successive steps of a method for person identification, according to an embodiment of the invention, 
         FIG. 4  represents two consecutive images of a video stream from a camera of the infrastructure of  FIG. 1 , 
         FIG. 5  represents person zones defined in the two images of  FIG. 4 , 
         FIG. 6  represents tracklets obtained from the person zones of  FIG. 5 , 
         FIG. 7  represents number zones defined in the person zones of  FIG. 6 , 
         FIG. 8  represents the recognition results of numbers present in the number zones of  FIG. 7 , 
         FIG. 9  represents different lines of division of the images, 
         FIG. 10  shows the determination of a person zone crossing one of the lines of  FIG. 9  having been selected, and 
         FIG. 11  shows the two images of  FIG. 4  after modification. 
     
    
    
     Referring to  FIG. 1 , a foot race infrastructure  100  implementing the invention will now be described. 
     The infrastructure  100  firstly comprises a route  102  designed to be covered by participants  106  in a race, for example a foot race. One of more crossing lines  104  are distributed along the route  102  so as to be crossed by the participants  106 , for example in order to obtain intermediate progression times during the race. Each crossing line  104  is fixed, i.e. it is always positioned in the same location along the route  102 , at least for the duration of the race. Moreover, each crossing line  104  may be virtual, i.e. it may not be marked on the route  102 . Each crossing line  104  is for example a straight line. 
     The infrastructure  100  further comprises a system  108  for detecting participants  106  in the race. 
     The system  108  firstly comprises one or more cameras  110  positioned along the route  102  so as to respectively point towards the crossing line or lines  104 , in order to detect the crossing of participants  106  and thus monitor their progression in the race. Thus, each camera  110  is associated with a respective crossing line  104 . The camera or cameras  110  are preferably fixed, like the crossing line or lines  104 . Preferably, each camera is positioned at height, for example between two and three meters high, and angled towards the participants, in order to be able to recognize them. 
     The system  108  further comprises a device  112  for the video surveillance of the crossing of a line by people. The device  112  is connected to each camera  110 , by a wired or wireless communication network. The device  112  is for example a computer, preferably equipped with one or more graphics cards and connected by Ethernet to the cameras  110 . This computer does not require an Internet connection. 
     In its simplest version, the system  108  comprises a single camera pointing towards a single crossing line  104 . The latter may be crossed several times by the participants, thus making it possible to collect several intermediate times at different milestones along the race. In this case, the route  102  must be closed (in a loop or figure-of-eight) and covered several times by the participants, so that they pass the crossing line  104  several times. 
     A more advanced version includes the installation of a high-speed wireless network between, on the one hand, the cameras  110 , distributed over several crossing lines, and, on the other hand, the computer responsible for processing the data. The data is then transferred via a high-speed long-range wireless network such as WiMAX (˜10-30 km) or using long-range WiFi technologies (˜2-10 km). 
     Referring to  FIG. 2 , the device  112  will now be described in more detail. 
     The device  112  firstly comprises video conversion means  202  designed to receive the video stream F from each camera  110  and to convert this video stream F into a series of successive images I. The images I are respectively associated with the moments (date and/or time) at which they were converted. Each video stream F is for example in RTSP (Real Time Streaming Protocol) format. 
     The device  112  further comprises means for locating a person  204  designed, for each of the successive images I of the video stream F of each camera  110 , to search for at least one person present in the image I and to define, in the image I, for each person found, a zone, known as a person zone ZP, surrounding this person at least partially. Each person zone ZP thus has a certain position in the image I. In the example described, each person zone ZP is a rectangular box surrounding the person and the position of this box in the image I is for example defined by the position of one of its corners. In the example described, the means for locating a person  204  comprise a neural network, for example a convolutional neural network with a Single Shot Multibox Detector or SSD. In the example described, the neural network has been previously trained to detect several targets, for example: pedestrian, two-wheeled vehicles, cars, trucks, other. Within the scope of the present invention, only the detection of pedestrians is used. 
     The device  112  further comprises means for constructing tracklets  206  designed, for each of at least one person found, to group together, into one tracklet, several person zones ZP from successive images I and surrounding the same person at least partially. 
     The device  112  further comprises means designed, for each tracklet T, to identify the person in this tracklet T from person zones ZP in this tracklet T. These means comprise the following means  208  to  224 . 
     Thus, the device  112  comprises number locating means  208  (Rib Number Detection or RBN Detection) designed, for each person zone ZP in the tracklet T, to search for at least one number present in the person zone ZP and to define, in the person zone ZP, for each number found, a zone, known as a number zone ZN, surrounding this number. In the example described, the number zone ZN is a rectangular box surrounding the number. In the present invention, the term “number” encompasses any series of characters and is therefore not limited only to series of digits. In the example described, the number locating means  208  comprise a neural network, for example a Deep Neural Network or DNN, trained beforehand to perform the tasks above. For example, the neural network is that described in the SSD-tensorflow project with the hyperparameters of the following Table 1: 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
             
            
               
                 CUDA_VISIBLE_DEVICES=0,1,2,3 setsid python Textbox_train.py \ 
               
               
                 --train_dir=${TRAIN_DIR} \ 
               
               
                 --dataset_dir=${DATASET_DIR} \ 
               
               
                 --save_summaries_secs=60 \ 
               
               
                 --save_interval_secs=1800 \ 
               
               
                 --weight_decay=0.0005 \ 
               
               
                 --optimizer=momentum \ 
               
               
                 --learning_rate=0.001 \ 
               
               
                 --batch_size=8 \ 
               
               
                 --num_samples=800000 \ 
               
               
                 --gpu_memory_fraction=0.95 \ 
               
               
                 --max_number_of_steps=500000 \ 
               
               
                 --use_batch=False \ 
               
               
                 --num_clones=4 \ 
               
               
                   
               
            
           
         
       
     
     The device  112  further comprises number recognition means  210  (Rib Number Recognition or RBN Recognition) designed, for each number zone ZN in the tracklet T, to recognize the number N° present in the number zone ZN. The number recognition means  210  are further designed, for each number N° recognized, to assess a reliability (also called “confidence”) of the recognition. In the example described, the number recognition means  210  comprise a neural network, for example a deep neural network, trained beforehand to perform the previous tasks. For example, the neural network is that of the CRNN_Tensorflow model as described in the article by Baoguang Shi et al. entitled “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition” and published on Jul. 21, 2015 on arXiv.org (https://arxiv.org/abs/1507.05717). 
     The device  112  further comprises number selection means  212  designed to select one of the numbers N° recognized from the reliabilities of these recognized numbers N°. 
     In the example described, the number selection means  212  are firstly designed to filter each number N° for which the reliability is below a predefined threshold. Thus, only the numbers N° with a reliability above the threshold, known as reliable numbers, are retained. The number selection means  212  are further designed to select one of the reliable numbers N° from their associated reliabilities. Regarding the latter selection, the number selection means  212  are for example designed to determine, from the values of the reliable numbers N°, the one for which a combination, such as the sum or even the average, of the reliabilities of the numbers having this value is the highest. The number N° selected by the number selection means  212  is then the one having this determined value. 
     The device  112  further comprises a database  214  comprising a set of predefined identification numbers identifying respective people. For example, in this database  214 , names N of participants  106  in the race are respectively associated with identification numbers. An example database  214  is illustrated in the following table: 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Name (N) 
                 Identification number 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Alice 
                 4523 
               
               
                   
                 Bob 
                 1289 
               
               
                   
                   
               
            
           
         
       
     
     The database  214  may further associate with each person (Name/N°) one or more reference visual signatures S R  representative of the visual appearance of this person. These reference visual signatures S R  may be recorded in the database  214  before the race and supplemented by other reference visual signatures S R  during the race, as will be explained below. 
     The device  112  further comprises name recovery means  216  comprising a first module  218  designed to search, among the identification numbers predefined in the database  214 , for the number N° selected by the number selection means  212  and to recover the associated name N. 
     The device  112  further comprises visual signature determination means  220  designed, for each person zone ZP in each tracklet T, to determine what is known as a local, visual signature S, of the person present in the person zone ZP and to assess a reliability (or “confidence”) of each local visual signature S. The local visual signature S of the person is representative of his or her overall visual appearance (which includes for example: the body type of the person, the shape and the color of their clothing, etc.). In the example described, the visual signature determination means  220  comprise a neural network, for example a deep neural network, trained beforehand to perform the previous tasks. For example, the neural network is the ResNet network  50 . Preferably, the neural network is trained beforehand from images of people in at least some of which the face of the person is not visible. Thus, the neural network successfully learns to recognize a person on the basis of their overall visual appearance, and not from the visual appearance of their face. 
     The device  112  further comprises means for determining an aggregate visual signature  222  designed, for each tracklet T, to determine an aggregate visual signature S A  from local visual signatures S of the person in the person zones ZP in the tracklet T and their associated reliabilities. For example, the aggregate visual signature S A  is an average of the local visual signatures S of the person in the person zones ZP of the tracklet T, weighted by the respective reliabilities of these local visual signatures S. 
     The means for determining the aggregate visual signature  222  are further designed to verify whether it has been possible to identify the person in the tracklet T by an identification number worn by this person, by the means previously described. 
     In this case, the means for determining an aggregate visual signature  222  are further designed to record the aggregate visual signature S A  in the database  214  and to associate it with the identification number found (and therefore also with the name N associated with this identification number). The aggregate visual signature S A  thus becomes a reference signature S R  for the person having this name N and identified by this identification number. 
     Otherwise, particularly if the reliabilities of the numbers N° assessed by the number recognition means  210  are all below the predefined threshold for the number selection means  212 , the means for determining the aggregate visual signature  222  are designed to supply this aggregate signature S A  to the name recovery means  216 . Indeed, the latter comprise, in addition to the first module  218 , a second module  224  designed to determine, in the database  214 , the identification number associated with one or more reference visual signatures S R  having a distance (for example, a Euclidean distance) in relation to the aggregate visual signature S A  below a predefined threshold. The second module  214  is further designed to record the aggregate visual signature S A  in the database  214  and to associate it with the identification number found (and therefore also with name N associated with this identification number). The aggregate visual signature S A  thus becomes a reference signature S R  for the person having this name N and identified by this identification number. The second module  224  is further designed to supply the identification number determined to the first module  218 , so that the latter recovers the name N associated with this identification number. 
     The device  112  further comprises line selection means  226  designed to receive, with each video stream F received, an identifier ID of the camera  110  sending this video stream F and to select a line L representing the crossing line  104  associated with the camera  110  having this camera identifier ID. Each line L has a fixed and predefined position in the images I provided by this camera  110 . The lines L are for example straight lines and/or divide each image I into two: an upstream part via which the participants  106  in the race are intended to arrive in the images I and a downstream part via which the participants  106  are intended to leave the images I. 
     The device  112  further comprises crossing detection means  228  designed firstly to determine, for each tracklet T, from the person zones ZP in the tracklet T, the zone first crossing, in a predefined direction, the line L selected by the line selection means  226 . For example, when the line L divides each image I into two parts, the crossing detection means  228  are designed, for each tracklet T, to determine the person zone ZP extending at least partly into the downstream part, whereas all the previous person zones ZP extended into the upstream part. 
     The crossing detection means  228  are further designed to determine a moment D at which the line L is crossed from a moment associated with the image I containing the person zone ZP crossing the line first. This moment of crossing D is for example the conversion moment associated with each image by the video conversion means  202 . 
     The device  112  further comprises image modification means  230  designed to add the name N provided by the name recovery means  216  and the moment of crossing D provided by the crossing detection means  228  in at least part of the images I containing the person zones ZP forming the tracklet T from which this name N and this moment of crossing D have been determined. This information N, D is for example added to the images I so as to obtain modified images I* in which the information N, D follows the person zone ZP. This makes it possible to implement augmented reality. 
     The device  112  further comprises video stream reconstruction means  232  designed to construct a reconstructed video stream F* from images I* modified by the image modification means  230  and from unmodified images I from the original video stream F (for example for the moments when no participant  106  is passing in front of the camera  110 ). 
     Referring to  FIGS. 3 to 11 , a method  300  for the video surveillance of the crossing of each crossing line  104  will now be described. During this description, a concrete example will be developed, for which certain results are illustrated in  FIGS. 4 to 11 . 
     During a step  302 , each camera  110  provides a video stream F to the device  112 . 
     During a step  304 , the video conversion means  202  receive the video stream F from each camera  110  and convert this video stream F into a series of successive images I. The video conversion means  202  further associate the images I with the respective moments at which they were converted. 
     Referring to  FIG. 4 , in the example developed, two successive images I 1 , I 2  obtained from the step  304  are illustrated. Two participants  1061 ,  1062  in the race are visible on these images I 1 , I 2 . 
     Back to  FIG. 3 , during a step  306 , for each of the images I of the video stream from each camera  110 , the means for locating a person  204  search for at least one person present in the image I and define, in the image I, for each person found, a person zone ZP surrounding this person at least partially. Thus, each person zone ZP defines, on the one hand, a sub-image (the content of the person zone ZP, i.e. the part of the image I contained in the person zone ZP) and occupies, on the other hand, a certain place in the image I (in particular a position in the image). 
     The result of the step  306  in the example developed is shown in  FIG. 5 . More specifically, for the image  11 , the means for locating a person  204  detect the first participant  1061  and define the participant zone ZP 11  around him or her. Moreover, the means for locating a person  204  detect the second participant  1062  and define the participant zone ZP 12  around him or her. The same occurs for the image  12 , giving rise to the participant zone ZP 21  surrounding the first participant  1061  and to the participant zone ZP 22  surrounding the second participant  1062 . 
     Back to  FIG. 3 , during a step  308 , for each of at least one person found, the means for constructing tracklets  206  group together, into one tracklet T, several person zones ZP from successive images I and surrounding the same person at least partially. 
     The result of step  308  in the example developed is illustrated in  FIG. 6 . More specifically, the means for constructing tracklets  206  supply a first tracklet T 1  grouping together the participant zones ZP 11 , ZP 12  surrounding the first participant  1061  and a second tracklet T 2  grouping together the person zones ZP 21 , ZP 22  surrounding the second participant  1062 . 
     Back to  FIG. 3 , the following steps  310  to  328  are implemented for each tracklet T, to identify the person in this tracklet T from person zones ZP in this tracklet T. 
     During a step  310 , for each person zone ZP in the tracklet T, the number locating means  208  search for at least one number N° present in the person zone ZP, and more specifically in the content of this person zone ZP, and define, in the person zone ZP, for each number N° found, a number zone ZN surrounding this number N°. 
     The result of step  310  in the example developed is shown in  FIG. 7 . More specifically, for the tracklet T 1 , the number locating means  208  detect a number in each person zone ZP 11 , ZP 12  and define, in respectively these two person zones ZP 11 , ZP 12 , the number zones ZN 11 , ZN 12 . Similarly, for the tracklet T 2 , the number locating means  208  detect a number in each person zone ZP 21 , ZP 22  and define, in respectively these two person zones ZP 21 , ZP 22 , the number zones ZN 21 , ZN 22 . 
     Back to  FIG. 3 , during a step  312 , for each number zone ZN in the tracklet T, the number recognition means  210  recognize the number N° present in the number zone ZN and assess the reliability of the recognition. 
     The result of step  312  in the example developed is shown in  FIG. 8 . More specifically, for the tracklet T 1 , the number recognition means  210  recognize the number  4523  in the number zone ZN 11  with a reliability of 73, and the number  4583  in the number zone ZN 12  with a reliability of 2. For the tracklet T 2 , the number recognition means  210  recognize the number  1289  in the number zone ZN 11 , with a reliability of 86 and the number  1289  in the number zone ZN 22 , with a reliability of 55. 
     Back to  FIG. 3 , during a step  314 , the number selection means  212  select one of the numbers N° recognized from the reliabilities associated with these recognized numbers N°. 
     In the example developed, in which pre-filtering is intended, the predefined threshold for filtering the numbers is 5. Thus, for the tracklet T 1 , the number  4583  in the number zone ZN 12  has a reliability below the predefined threshold and is therefore filtered by the number selection means  212 . All that remains is the number  4523  in the number zone ZN 11  which is therefore selected by the number selection means  212 . For the tracklet T 2 , the two numbers  1289  in the number zones ZN 21 , ZN 22  are reliable and are therefore not filtered by the number selection means  212 . These two numbers moreover have the same value, 1289. Thus, the number selection means  212  combine the reliabilities of these two numbers, for example by taking their average, which is 70.5. To show an example of selecting from several different numbers, it is assumed that the tracklet T 1  further comprises the two images preceding the images I 1 , I 2 , that these two images also contain the second participant  1062 , and that these two images respectively result in the following two number predictions: 7289 with a reliability of 70 and 7289 with a reliability of 50. The combination (average in the example described) of the reliabilities of the numbers having the value 7289 is therefore 60. Thus, in this example, the value 1289 is the one for which the combination of the reliabilities of numbers having this value is the highest and the number  1289  is therefore selected by the number selection means  212 . 
     During a step  316 , the name recovery means  216  search, among the identification numbers predefined in the database  214 , for the number N° selected in the step  314  and recover the associated name N. 
     In parallel to steps  310  to  316 , the following steps  318  to  328  are implemented. 
     During a step  318 , for each person zone ZP in the tracklet T, the visual signature determination means  220  determine, from the content of this person zone ZP, a local visual signature S of the person present in the person zone ZP and associate each local visual signature S with a reliability. 
     During a step  320 , the means for determining the aggregate visual signature  222  determine an aggregate visual signature S A  from local visual signatures S of the person in the person zones ZP of the tracklet T and of their associated reliabilities. 
     During a step  322 , the means for determining the aggregate visual signature  222  verify whether it has been possible to identify the person in the tracklet T by a number worn by this person. For example, the means for determining the aggregate visual signature  222  verify whether it has been possible to select a number N° in the step  314  and/or whether one of the identification numbers in the database  214  has been found in step  316 , making it possible to recover a name N. 
     If this is the case, during a step  324 , the means for determining the aggregate visual signature  222  record the aggregate visual signature S A  in the database  214  and associate it with the name of the person N (and with the associated number N°) recovered by the name recovery means  216 . The aggregate visual signature S A  then becomes a reference visual signature S R  representing the person having the name N, and supplementing the reference visual signatures already present in the database  214 , for example those recorded before the race or indeed those obtained during the race. 
     Otherwise, during a step  326 , the means for determining the aggregate visual signature  222  supply this aggregate visual signature S A  to the name recovery means  216 . 
     During a step  328 , the person in the tracklet T is identified from the aggregate visual signature S A . To do this, the name recovery means  216  determine, from the numbers N° predefined in the database  214 , the one associated with one or more reference visual signatures S R  having a distance with the aggregate visual signature S A  below a predefined threshold and recover the name of the person N associated with this number N°. In the event that a number N° is associated with several reference visual signatures S R , the distance of the aggregate visual signature S A  in relation to these reference visual signatures S R  is for example an average of the respective distances between the aggregate visual signature S A  and the reference visual signatures S R . Thus, if the number N° is associated with two reference visual signatures S R , the distance of the aggregate visual signature S A  in relation to these two reference visual signatures S R  is an average of the distance of the aggregate visual signature S A  to the first reference visual signature S R  and of the distance of the aggregate visual signature S A  to the second reference visual signature S R . If a number N° is found, the means for determining the aggregate visual signature  222  record the aggregate visual signature S A  in the database  214  and associate it with the name of the person N (and with the associated number N°) recovered by the name recovery means  216 . 
     In parallel to steps  310  to  316  and to steps  318  to  328 , the following steps  330  to  334  are implemented. 
     During a step  330 , the line selection means  226  receive, with the video stream F received, an identifier ID of the camera  110  sending this video stream F and select the line L representing the crossing line  104  associated with the camera  110  having this identifier ID. 
       FIG. 9  shows, in the scope of the example developed, three lines L 1 , L 2 , L 3  respectively associated with three cameras  110 . In this example, the line selection means  226  select the line L 3 . 
     Back to  FIG. 3 , during a step  332 , the crossing detection means  228  determine, from the person zones ZP in each tracklet T, the zone first crossing, in a predefined direction, the line L selected, i.e. the zone for which the position occupied is crossed first by the line L, whereas the position occupied by the person zone of the previous image ZP in the tracklet was (entirely) on a predefined side of the line L (for example on the upstream side of the line L). 
     Referring to  FIG. 10 , in the example developed, for the second participant  1062 , the person zone ZP 22  of the image  12  is the first to cross the line L 3  selected. More specifically, the person zone ZP 21  of the image  11  is (entirely) in the upstream part of the image  11  whereas a portion of the person zone ZP 22  of the image  12  is in the downstream part of the image  12 . 
     Back to  FIG. 3 , during a step  334 , the crossing detection means  228  determine a moment D at which the line L is crossed from a moment associated with the image I containing the person zone ZP crossing the line first. The moment of crossing D could further be determined from a moment associated with the previous image. 
     In the example developed, the moment of crossing D is also taken as the moment of conversion of the image  12  in step  304 . Alternatively, the moment of crossing D could be an intermediate moment between the moment associated with the image  12  and the moment associated with the image  11 . 
     During a step  336 , the image modification means  230  add the name N supplied by the name recovery means  216  and the moment of crossing D supplied by the crossing detection means  228  in at least some of the images I containing the person zones ZP forming the tracklet T from which this name N and this moment of crossing D have been determined. 
     The two modified images I* 1 , I* 2  obtained in step  336  in the example developed are shown in  FIG. 11 . 
     During a step  338 , the video stream reconstruction means  232  construct a reconstructed video stream F* from images I* modified by the image modification means  230  and from unmodified images I from the original video stream F. 
     It is clear that a method such as the one described above makes it possible to monitor the crossing of a line by people. 
     Moreover, it will be understood that each of the elements  202  to  232  described previously may be implemented using hardware, for example by microprogrammed or microcabled functions in dedicated integrated circuits (without a computer program), and/or using software, for example by one or more computer programs intended to be executed by one or more computers each comprising, on the one hand, one or more memories for storing data files and one or more of these computer programs and, on the other hand, one or more processors associated with this or these memories and intended to execute the instructions of the computer program or programs stored in the memory or memories of this computer. 
     It should also be noted that the invention is not limited to the embodiments described above. It will indeed become evident to the person skilled in the art that various modifications can be made to the embodiment described above, in light of the findings that have just been disclosed to him/her. 
     For example, the elements  202  to  232  could be distributed between several computers. They could even be replicated in these computers. For example, provision could be made for a computer for each camera. In this case, each computer would repeat the elements of the device  112 , except the inputting of an identifier ID of the camera and the line selection means  226 , which would be unnecessary as this computer would only consider the line associated with the camera to which it is connected. In this case, the different computers are preferably synchronized with one another in order that they determine consistent crossing times between one camera and another. The NTP (Network Time Protocol) is used for example. 
     In the detailed presentation of the invention that is made above, the terms used shall not be interpreted as limiting the invention to the embodiment set out in this description, but shall be interpreted to include all the equivalents whose prediction is within the grasp of the person skilled in the art by applying his/her general knowledge to the implementation of the findings that have just been disclosed to him/her.