Intelligent diversion system and method for personnel and vehicles in scenic spots

Abstract

A system and method for intelligently guiding people and vehicles in scenic areas. First, keyframe discrete sampling is performed on crowd flow monitoring videos captured by cameras deployed at intersections in the scenic area to obtain a time series of crowd flow monitoring keyframes. Next, a crowd flow distribution feature extractor based on a deep neural network model extracts features from the time series of crowd flow monitoring keyframes to obtain a time series of crowd flow distribution semantic feature maps. The time series of crowd flow distribution semantic feature maps is then passed through a spatial information multi-scale dense retention attention module to obtain a time series of spatially significant crowd flow distribution semantic feature maps. Next, the time series of spatially significant crowd flow distribution semantic feature maps is passed through a crowd flow temporal distribution inference module to obtain crowd flow temporal inference semantic features. Finally, based on the crowd flow temporal inference semantic features, a determination is made as to whether to extend the green light time.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent vehicle diversion, and in particular to a system and method for intelligent diversion of people and vehicles in scenic spots. Background Art

[0002] With the rapid development of the tourism industry and increased population mobility, scenic areas are becoming densely populated with people and vehicles, severely impacting traffic efficiency and safety. Effective diversion allows visitors to enter and exit scenic areas more quickly and conveniently, reducing wait times and improving visitor satisfaction. Furthermore, proper diversion can reduce the incidence of traffic accidents and ensure the safety of tourists and employees. Therefore, managing the diversion of people and vehicles in scenic areas has become a key concern for managers.

[0003] However, traditional methods for directing people and vehicles through scenic areas rely heavily on manual experience and simple rules for decision-making. For example, adjusting green light times at scenic areas relies on manual experience and manipulation. However, manual intervention is easily influenced by the subjective consciousness and experience of supervisors, making it difficult to cope with complex fluctuations in human and vehicle traffic, and easily resulting in green light times that are too short or too long. Furthermore, traditional methods for directing people and vehicles through scenic areas lack intelligence, resulting in slow response times and often delayed responses. This makes them unable to respond promptly to emergencies or dramatic changes in crowd density, easily leading to traffic congestion and chaos.

[0004] Therefore, an optimized intelligent diversion system for people and vehicles in scenic spots is desired. Summary of the Invention

[0005] The present disclosure is made in consideration of the above problems. One purpose of the present disclosure is to provide a system and method for intelligently guiding people and vehicles in a scenic area.

[0006] The embodiments of the present disclosure provide a smart traffic diversion system for people and vehicles in a scenic area, which includes:

[0007] The crowd flow monitoring video acquisition module is used to obtain crowd flow monitoring videos collected by cameras deployed at intersections in scenic areas;

[0008] A crowd flow monitoring key frame sampling module is used to perform discrete key frame sampling on the crowd flow monitoring video to obtain a time series of crowd flow monitoring key frames;

[0009] A crowd distribution semantic feature extraction module is used to extract features from each crowd flow monitoring key frame in the time series of the crowd flow monitoring key frames using a crowd distribution feature extractor based on a deep neural network model to obtain a time series of crowd flow distribution semantic feature maps;

[0010] A crowd flow distribution semantic spatial feature attention module is used to pass the time series of the crowd flow distribution semantic feature map through a spatial information multi-scale intensive retention attention module to obtain a time series of spatially significant crowd flow distribution semantic feature maps;

[0011] A pedestrian flow temporal reasoning module, configured to pass the time series of the spatially significant pedestrian flow distribution semantic feature graph through the pedestrian flow temporal distribution reasoning module to obtain a pedestrian flow temporal reasoning semantic feature vector as a pedestrian flow temporal reasoning semantic feature; and

[0012] The green light time control module is used to determine whether to extend the green light time based on the pedestrian flow temporal reasoning semantic features.

[0013] For example, according to the intelligent guidance system for people and vehicles in scenic spots according to an embodiment of the present disclosure, the deep neural network model is a convolutional neural network model.

[0014] For example, according to the intelligent traffic diversion system for people and vehicles in scenic spots according to an embodiment of the present disclosure, the crowd distribution semantic space feature attention module includes:

[0015] A crowd distribution semantic feature map multi-mode pooling unit is used to perform multi-mode pooling processing on each crowd distribution semantic feature map in the time series of the crowd distribution semantic feature map along the channel dimension to obtain a spliced ​​crowd distribution semantic feature matrix;

[0016] A crowd flow distribution implicit correlation feature capture unit, configured to perform convolution processing on the spliced ​​crowd flow distribution semantic feature matrix through a convolution layer to obtain a crowd flow distribution implicit correlation feature matrix;

[0017] a crowd flow distribution spatial attention weight calculation activation unit, configured to input the crowd flow distribution implicit correlation feature matrix into a Sigmoid activation unit to obtain a crowd flow distribution spatial attention weight matrix; and

[0018] The spatial saliency enhancement unit is used to multiply each feature matrix along the channel dimension in the crowd distribution semantic feature map by the crowd distribution spatial attention weight matrix at each position point to obtain the spatially salient crowd distribution semantic feature map.

[0019] For example, according to the intelligent traffic diversion system for people and vehicles in a scenic area according to an embodiment of the present disclosure, the multi-mode pooling unit of the semantic feature graph of the crowd distribution includes:

[0020] A global mean pooling subunit, configured to perform global mean pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic global mean pooling feature matrix;

[0021] A maximum pooling subunit is used to perform maximum pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic maximum pooling feature matrix;

[0022] a random pooling subunit, configured to perform random pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic random pooling feature matrix; and

[0023] A multi-pooling semantic splicing unit is used to splice the crowd flow distribution semantic mean pooling feature matrix, the crowd flow distribution semantic maximum pooling feature matrix and the crowd flow distribution semantic random pooling feature matrix to obtain the spliced ​​crowd flow distribution semantic feature matrix.

[0024] For example, according to the intelligent traffic diversion system for people and vehicles in scenic spots according to an embodiment of the present disclosure, the people flow temporal reasoning module includes:

[0025] a vectorization expansion unit, configured to vectorize and expand a plurality of spatially significant crowd flow distribution semantic feature maps in the time series of the spatially significant crowd flow distribution semantic feature maps to obtain a sequence of spatially significant crowd flow distribution semantic feature vectors;

[0026] a vector screening unit, configured to determine the spatially significant crowd flow distribution semantic feature vector at the last position in the sequence of the spatially significant crowd flow distribution semantic feature vectors;

[0027] a crowd flow distribution average information difference calculation unit, configured to calculate a positional difference between a mean feature vector of a plurality of spatially significant crowd flow distribution semantic feature vectors in the sequence of the spatially significant crowd flow distribution semantic feature vectors and the spatially significant crowd flow distribution semantic feature vector at the last position to obtain a crowd flow distribution average information difference feature vector;

[0028] a crowd flow distribution random information difference calculation unit, configured to calculate a positional difference between a spatially significant crowd flow distribution semantic feature vector at a random position in the sequence of the spatially significant crowd flow distribution semantic feature vectors and the spatially significant crowd flow distribution semantic feature vector at the last position to obtain a crowd flow distribution random information difference feature vector;

[0029] A first crowd flow distribution inference unit is configured to multiply the crowd flow distribution average information difference feature vector by a first predetermined weight hyperparameter and a first Gaussian distribution random number function value to obtain a first crowd flow distribution inference feature vector;

[0030] A second crowd flow distribution inference unit is configured to multiply the crowd flow distribution random information difference feature vector by a second predetermined weight hyperparameter and a second Gaussian distribution random number function value to obtain a second crowd flow distribution inference feature vector; and

[0031] A fusion unit is used to calculate the positional addition between the first crowd flow distribution reasoning feature vector and the second crowd flow distribution reasoning feature vector to obtain the crowd flow temporal reasoning semantic feature vector.

[0032] For example, according to the intelligent guidance system for personnel and vehicles in a scenic area according to an embodiment of the present disclosure, the first Gaussian distribution random number function value and the second Gaussian distribution random number function value are generated by a Gaussian distribution random number function with a mean of 0 and a variance of 1.

[0033] For example, according to the intelligent traffic diversion system for people and vehicles in scenic spots according to an embodiment of the present disclosure, the green light time control module is used to:

[0034] The pedestrian flow temporal reasoning semantic feature vector is passed through a traffic light controller based on a classifier to obtain a control instruction, where the control instruction is used to indicate whether to extend the green light time.

[0035] For example, according to the embodiment of the present disclosure, the intelligent traffic diversion system for scenic area personnel and vehicles also includes a training module for training the crowd distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale intensive retention attention module, the crowd flow temporal distribution inference module and the classifier-based traffic light controller.

[0036] For example, according to the intelligent guidance system for people and vehicles in scenic spots according to an embodiment of the present disclosure, the training module includes:

[0037] A training data acquisition unit, configured to acquire training data, wherein the training data includes training pedestrian flow monitoring videos collected by cameras deployed at intersections in scenic areas;

[0038] A training crowd flow monitoring key frame sampling unit is used to perform key frame discrete sampling on the training crowd flow monitoring video to obtain a time series of training crowd flow monitoring key frames;

[0039] A training crowd flow distribution semantic feature extraction unit is used to extract features from each training crowd flow monitoring key frame in the time series of the training crowd flow monitoring key frames through the crowd flow distribution feature extractor based on the convolutional neural network model to obtain a time series of training crowd flow distribution semantic feature maps;

[0040] A training crowd flow distribution semantic space feature attention unit is used to pass the time series of the training crowd flow distribution semantic feature map through the spatial information multi-scale dense retention attention module to obtain a time series of training spatially significant crowd flow distribution semantic feature map;

[0041] A training pedestrian flow temporal reasoning unit is used to pass the time series of the training spatially significant pedestrian flow distribution semantic feature map through the pedestrian flow temporal distribution reasoning module to obtain a training pedestrian flow temporal reasoning semantic feature vector;

[0042] a loss calculation unit, configured to pass the training pedestrian flow temporal reasoning semantic feature vector through the classifier-based traffic light controller to obtain a classification loss function value; and

[0043] A loss training unit is used to train the crowd distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale dense retention attention module, the crowd flow temporal distribution reasoning module and the classifier-based traffic light controller based on the classification loss function value, wherein in each round of training iteration, the training crowd flow temporal reasoning semantic feature vector is corrected.

[0044] The embodiments of the present disclosure further provide a method for intelligently directing people and vehicles in a scenic area, which includes:

[0045] Obtain pedestrian flow monitoring videos collected by cameras deployed at intersections in scenic areas;

[0046] Performing discrete key frame sampling on the crowd flow monitoring video to obtain a time series of crowd flow monitoring key frames;

[0047] Performing feature extraction on each of the crowd flow monitoring key frames in the time series of the crowd flow monitoring key frames using a crowd flow distribution feature extractor based on a deep neural network model to obtain a time series of crowd flow distribution semantic feature maps;

[0048] The time series of the crowd flow distribution semantic feature map is passed through a spatial information multi-scale dense retention attention module to obtain a time series of spatially significant crowd flow distribution semantic feature map;

[0049] Passing the time series of the spatially significant crowd flow distribution semantic feature graph through a crowd flow temporal distribution reasoning module to obtain a crowd flow temporal reasoning semantic feature vector as a crowd flow temporal reasoning semantic feature; and

[0050] Based on the pedestrian flow temporal reasoning semantic features, it is determined whether to extend the green light time.

[0051] According to the embodiments of the present invention, the intelligent personnel and vehicle diversion system and method for scenic spots are equipped with cameras arranged at intersections of scenic spots to monitor and collect crowd flow monitoring videos in real time, and introduce image processing and analysis algorithms based on artificial intelligence and machine vision at the back end to analyze the crowd flow monitoring videos to capture semantic features related to crowd distribution and density. Then, by enhancing the crowd distribution semantics extracted from the crowd flow monitoring videos based on multi-scale correlation features in the time-space dimension, the temporal pattern of crowd distribution is inferred and the next crowd flow situation is predicted, so as to be able to intelligently adjust the green light time, improve the operating efficiency of the scenic spot and the tourist experience, and reduce the probability of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings of the embodiments of the present disclosure. Obviously, the drawings described below only relate to some embodiments of the present disclosure, and are not intended to limit the present disclosure.

[0053] Figure 1 A schematic diagram of the application architecture of the intelligent traffic diversion system for people and vehicles in scenic spots according to an embodiment of the present disclosure is shown;

[0054] Figure 2 A schematic diagram of the structure of the intelligent diversion system for people and vehicles in a scenic area according to an embodiment of the present disclosure is shown;

[0055] Figure 3 A schematic diagram showing the structure of a crowd distribution semantic space feature attention module of a scenic area personnel and vehicle intelligent diversion system according to an embodiment of the present disclosure is shown;

[0056] Figure 4 A schematic diagram of the structure of a pedestrian flow time sequence reasoning module of the intelligent traffic diversion system for people and vehicles in a scenic area according to an embodiment of the present disclosure is shown;

[0057] Figure 5 A flow chart of a method for intelligently directing people and vehicles in a scenic area according to an embodiment of the present disclosure is shown;

[0058] Figure 6 An application scenario diagram of the intelligent traffic diversion system for people and vehicles in a scenic area according to an embodiment of the present disclosure is shown; and

[0059] Figure 7 A schematic diagram of a storage medium according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present disclosure, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts are also within the scope of protection of the present disclosure.

[0061] The terms used in this specification are those commonly used in the art in view of the functions of the present disclosure, but these terms may vary according to the intentions of those skilled in the art, precedents, or new technologies in the art. In addition, specific terms may be selected by the applicant, and in such cases, their detailed meanings will be described in the detailed description of the present disclosure. Therefore, the terms used in the specification should not be understood as simple names, but rather as a general description based on the meaning of the terms and the present disclosure.

[0062] Although the present disclosure makes various references to certain modules in the system according to embodiments of the present disclosure, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0063] Flowcharts are used throughout this disclosure to illustrate the operations performed by systems according to embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0064] Figure 1 A schematic diagram of the application architecture of the intelligent guidance system for people and vehicles in a scenic area in an embodiment of the present disclosure is shown, including a server 100 and a terminal device 200.

[0065] The terminal device 200 and the server 100 can be connected via the Internet to achieve mutual communication. Optionally, the above-mentioned Internet uses standard communication technologies and / or protocols. The Internet is typically the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network. In some embodiments, technologies and / or formats including Hypertext Markup Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged over the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0066] The server 100 can provide various network services to the terminal device 200. The server 100 can be a single server, a server cluster consisting of multiple servers, or a cloud computing center. Specifically, the server 100 may include a processor 110 (Center Processing Unit, CPU), a memory 120, an input device 130, and an output device 140. The input device 130 may include a keyboard, a mouse, a touch screen, etc. The output device 140 may include a display device such as a liquid crystal display (LCD) or a cathode ray tube (CRT).

[0067] The memory 120 may include a read-only memory (ROM) and a random access memory (RAM), and provides program instructions and data stored in the memory 120 to the processor 110. In the embodiment of the present disclosure, the memory 120 may be used to store the method corresponding to the intelligent guidance system for scenic area personnel and vehicles in the embodiment of the present disclosure.

[0068] The processor 110 calls the program instructions stored in the memory 120, and the processor 110 is used to execute the steps of any one of the methods for intelligently directing people and vehicles in scenic spots in the embodiments of the present disclosure according to the obtained program instructions.

[0069] In addition, the application architecture diagram in the embodiment of the present disclosure is intended to more clearly illustrate the technical solution in the embodiment of the present disclosure, and does not constitute a limitation on the technical solution provided by the embodiment of the present disclosure. Of course, for other application architectures and business applications, the technical solution provided by the embodiment of the present disclosure is also applicable to similar problems.

[0070] The following non-restrictive description of the intelligent diversion system for personnel and vehicles in a scenic area provided according to at least one embodiment of the present disclosure is given by means of several examples or embodiments. As described below, different features in these specific examples or embodiments can be combined with each other without conflicting with each other to obtain new examples or embodiments, and these new examples or embodiments also fall within the scope of protection of the present disclosure.

[0071] In response to the above technical problems, the technical solution of this application proposes a smart traffic diversion system for people and vehicles in scenic areas. The system can collect real-time crowd flow monitoring videos through cameras placed at intersections in scenic areas, and introduce image processing and analysis algorithms based on artificial intelligence and machine vision at the back end to analyze the crowd flow monitoring videos to capture semantic features related to crowd flow distribution and density. Then, by enhancing the crowd flow distribution semantics extracted from the crowd flow monitoring videos based on multi-scale correlation features in the temporal-spatial dimension, the temporal pattern of crowd flow distribution is inferred and the subsequent crowd flow situation is predicted, so that the green light time can be adjusted intelligently, the operating efficiency of the scenic area and the visitor experience can be improved, and the probability of accidents can be reduced.

[0072] Figure 2The structure diagram of the intelligent flow diversion system 800 for scenic area personnel and vehicles in the embodiment of the present disclosure is shown. The intelligent flow diversion system 800 for scenic area personnel and vehicles includes: a crowd monitoring video acquisition module 810 for acquiring crowd monitoring videos acquired by cameras deployed at intersections in the scenic area; a crowd monitoring key frame sampling module 820 for discretely sampling key frames of the crowd monitoring video to obtain a time series of crowd monitoring key frames; a crowd distribution semantic feature extraction module 830 for extracting features from each crowd monitoring key frame in the time series of the crowd monitoring key frames using a crowd distribution feature extractor based on a deep neural network model to obtain a crowd distribution semantic feature map. time series; a crowd distribution semantic spatial feature attention module 840, which is used to pass the time series of the crowd distribution semantic feature map through the spatial information multi-scale intensive retention attention module to obtain a time series of spatially significant crowd distribution semantic feature map; a crowd temporal reasoning module 850, which is used to pass the time series of the spatially significant crowd distribution semantic feature map through the crowd flow temporal distribution reasoning module to obtain a crowd flow temporal reasoning semantic feature vector as a crowd flow temporal reasoning semantic feature; and a green light time control module 860, which is used to determine whether to extend the green light time based on the crowd flow temporal reasoning semantic feature.

[0073] Specifically, in the technical solution of the present application, first, a crowd flow monitoring video collected by a camera deployed at a scenic area intersection is obtained. Next, considering that the crowd flow monitoring video is usually a continuous video stream, it contains a large amount of redundant information and interference information. Therefore, it is necessary to perform key frame discrete sampling on the crowd flow monitoring video to obtain a time series of crowd flow monitoring key frames. In particular, here, the key frame refers to a representative frame image in the video, which contains important crowd flow distribution information in crowd flow monitoring, such as personnel density, flow direction, surge in crowd flow and periodic change nodes, etc. Through key frame discrete sampling, the amount of data processed for video data can be effectively reduced, the data processing efficiency can be improved, and these key crowd flow distribution information can be better retained and analyzed, which is beneficial to the diversion management of personnel and vehicles in the scenic area.

[0074] Then, considering that each of the crowd monitoring key frames in the time series of the crowd monitoring key frames contains semantic information about crowd distribution, such as crowd density, crowd flow direction, etc., this will help to further understand the semantics and temporal change patterns of crowd distribution. Based on this, in the technical solution of the present application, each of the crowd monitoring key frames in the time series of the crowd monitoring key frames is subjected to feature mining in a crowd distribution feature extractor based on a convolutional neural network model to extract the crowd distribution semantic feature information in each of the crowd monitoring key frames, thereby obtaining a time series of crowd distribution semantic feature maps. The crowd distribution semantic feature map contains semantic features about crowd distribution under specific key frames, which can provide richer information support for the subsequent capture of the temporal pattern and change trend of crowd distribution, and is conducive to more accurate green light time adjustment and crowd guidance.

[0075] Correspondingly, in the crowd distribution semantic feature extraction module 830, the deep neural network model is a convolutional neural network model, that is, the crowd distribution feature extractor based on the deep neural network model is a crowd distribution feature extractor based on the convolutional neural network model.

[0076] Furthermore, considering that in the actual process of crowd flow monitoring and crowd flow distribution reasoning, it is necessary to focus more on the spatial position characteristics of the crowd flow distribution in each key frame, such as information such as dense crowd flow areas and crowd flow paths. Based on this, in the technical solution of the present application, the time series of the crowd flow distribution semantic feature map is further passed through the spatial information multi-scale dense retention attention module to obtain the time series of spatially significant crowd flow distribution semantic feature map. It should be understood that the processing of the spatial information multi-scale dense retention attention module can help the model better focus on the spatially significant information in the crowd flow distribution key frame, that is, highlight and emphasize the areas of great significance such as dense crowd flow areas and crowd flow paths. At the same time, the multi-scale dense retention attention module can also capture the spatial feature information at different scales in the key frame, and aggregate and integrate this information, so that the model can understand the semantics of crowd flow distribution more comprehensively and improve the representation ability of complex scenes. In this way, it can provide a more spatially significant crowd flow distribution semantic feature representation for subsequent decision-making, help to more finely analyze the spatial distribution characteristics of crowd flow, and provide more powerful support for intelligent decision-making.

[0077] Accordingly, if Figure 3As shown, the crowd distribution semantic space feature attention module 840 includes: a crowd distribution semantic feature map multi-mode pooling unit 841, which is used to perform multi-mode pooling processing on each crowd distribution semantic feature map in the time series of the crowd distribution semantic feature map along the channel dimension to obtain a spliced ​​crowd distribution semantic feature matrix; a crowd distribution implicit correlation feature capture unit 842, which is used to perform convolution processing on the spliced ​​crowd distribution semantic feature matrix through a convolution layer to obtain a crowd distribution implicit correlation feature matrix; a crowd distribution spatial attention weight calculation activation unit 843, which is used to input the crowd distribution implicit correlation feature matrix into a Sigmoid activation unit to obtain a crowd distribution spatial attention weight matrix; and a spatial saliency enhancement unit 844, which is used to perform positional point multiplication of each feature matrix along the channel dimension in the crowd distribution semantic feature map with the crowd distribution spatial attention weight matrix to obtain the spatially salient crowd distribution semantic feature map.

[0078] Among them, the multi-mode pooling unit 841 of the crowd distribution semantic feature map includes: a global mean pooling subunit, which is used to perform global mean pooling processing on the crowd distribution semantic feature map along the channel dimension to obtain a crowd distribution semantic global mean pooling feature matrix; a maximum pooling subunit, which is used to perform maximum pooling processing on the crowd distribution semantic feature map along the channel dimension to obtain a crowd distribution semantic maximum pooling feature matrix; a random pooling subunit, which is used to perform random pooling processing on the crowd distribution semantic feature map along the channel dimension to obtain a crowd distribution semantic random pooling feature matrix; and a multi-pooling semantic splicing unit, which is used to splice the crowd distribution semantic mean pooling feature matrix, the crowd distribution semantic maximum pooling feature matrix and the crowd distribution semantic random pooling feature matrix to obtain the spliced ​​crowd distribution semantic feature matrix.

[0079] Specifically, the crowd flow distribution semantic spatial feature attention module 840 is used to process the time series of the crowd flow distribution semantic feature map through the spatial information multi-scale dense retention attention module using the following spatial explicitation formula to obtain the time series of the spatially significant crowd flow distribution semantic feature map; wherein the spatial explicitation formula is:

[0080]

[0081] Among them, F2 is each crowd flow distribution semantic feature graph in the time series of the crowd flow distribution semantic feature graph, f N ×N represents the convolution kernel size, Respectively represent the mean pooling feature matrix, maximum pooling feature matrix and random pooling feature matrix obtained by performing global mean pooling, global maximum pooling and global random pooling on each of the crowd flow distribution semantic feature maps along the channel dimension, σ is the Sigmoid activation function, M s (F2) is the attention weight matrix of each of the crowd distribution semantic feature maps, represents the Hadamard product, and F2′ is each spatially significant crowd flow distribution semantic feature map in the time series of the spatially significant crowd flow distribution semantic feature map.

[0082] It should be understood that since the change of pedestrian flow has obvious temporal characteristics, that is, there is a temporal correlation between the semantic features of the pedestrian flow distribution under each key frame after spatial saliency enhancement. Therefore, in order to better characterize the temporal distribution characteristics of the semantics of the spatial distribution of pedestrian flow, including semantic information on the time dimension such as the temporal fluctuation, trend and periodicity of pedestrian flow, and provide richer feature representation for subsequent analysis and decision-making, in the technical solution of the present application, the time series of the spatially significant pedestrian flow distribution semantic feature map is further passed through the pedestrian flow temporal distribution reasoning module to obtain the pedestrian flow temporal reasoning semantic feature vector. Through the processing of the pedestrian flow temporal distribution reasoning module, the semantic features of the pedestrian flow distribution of each key frame after spatial saliency enhancement can be temporally associated and understood, so as to better understand the dynamic change law of the pedestrian flow distribution semantics, and use the temporal correlation features of the spatial distribution of pedestrian flow as prior information to perform pedestrian flow reasoning and prediction in the next short period of time, thereby providing richer information support for subsequent decision-making, such as intelligently adjusting the green light time, optimizing pedestrian flow guidance, etc., and improving the intelligent level of scenic area management.

[0083] Accordingly, if Figure 4As shown, the crowd flow time series reasoning module 850 includes: a vectorization expansion unit 851, which is used to vectorize and expand multiple spatially significant crowd flow distribution semantic feature maps in the time series of the spatially significant crowd flow distribution semantic feature map to obtain a sequence of spatially significant crowd flow distribution semantic feature vectors; a vector screening unit 852, which is used to determine the spatially significant crowd flow distribution semantic feature vector of the last position in the sequence of the spatially significant crowd flow distribution semantic feature vectors; a crowd flow distribution average information difference calculation unit 853, which is used to calculate the position difference between the mean feature vector of multiple spatially significant crowd flow distribution semantic feature vectors in the sequence of the spatially significant crowd flow distribution semantic feature vectors and the spatially significant crowd flow distribution semantic feature vector of the last position to obtain the crowd flow distribution average information difference feature vector; a crowd flow distribution random information difference calculation unit 854, which is used to calculate the spatial The spatially significant crowd flow distribution semantic feature vectors at random positions in the sequence of significant crowd flow distribution semantic feature vectors are calculated by positional difference between the spatially significant crowd flow distribution semantic feature vectors at the last position and the spatially significant crowd flow distribution semantic feature vectors at the last position to obtain a crowd flow distribution random information difference feature vector; a first crowd flow distribution inference unit 855 is configured to multiply the crowd flow distribution average information difference feature vector by a first predetermined weight hyperparameter and a first Gaussian distribution random number function value to obtain a first crowd flow distribution inference feature vector; a second crowd flow distribution inference unit 856 is configured to multiply the crowd flow distribution random information difference feature vector by a second predetermined weight hyperparameter and a second Gaussian distribution random number function value to obtain a second crowd flow distribution inference feature vector; and a fusion unit 857 is configured to calculate the positional addition between the first crowd flow distribution inference feature vector and the second crowd flow distribution inference feature vector to obtain the crowd flow time series inference semantic feature vector. Wherein, the first Gaussian distribution random number function value and the second Gaussian distribution random number function value are generated by a Gaussian distribution random number function with a mean of 0 and a variance of 1.

[0084] Specifically, the pedestrian flow temporal reasoning module 850 is configured to process the time series of the spatially significant pedestrian flow distribution semantic feature map through the pedestrian flow temporal distribution reasoning module using the following reasoning formula to obtain the pedestrian flow temporal reasoning semantic feature vector; wherein the reasoning formula is:

[0085]

[0086] Wherein, V1 is the spatially significant crowd flow distribution semantic feature vector at the last position of the sequence of spatially significant crowd flow distribution semantic feature vectors obtained by vectorizing and expanding multiple spatially significant crowd flow distribution semantic feature maps in the time series of the spatially significant crowd flow distribution semantic feature map, V randis the spatially significant crowd flow distribution semantic feature vector at a random position in the sequence of the spatially significant crowd flow distribution semantic feature vectors, V r is the mean feature vector of the sequence of the spatially significant crowd flow distribution semantic feature vectors, p1 and p2 are weight hyperparameters, f1 and f2 are hyperparameters that generate Gaussian distribution random number functions with a mean of 0 and a variance of 1 as Gaussian distribution function coefficients, and are vector subtraction and vector addition, V i+1 is the semantic feature vector for the pedestrian flow temporal reasoning.

[0087] The pedestrian flow temporal reasoning semantic feature vector is then passed through a classifier-based traffic light controller to generate a control instruction indicating whether to extend the green light duration. In other words, by using the temporal reasoning semantic features of pedestrian flow to perform classification processing, the decision on whether to extend the green light duration is made. By intelligently adjusting the green light duration, the efficiency of scenic spots and the visitor experience can be improved, while also reducing the likelihood of accidents.

[0088] Correspondingly, the green light time control module 860 is used to: pass the pedestrian flow temporal reasoning semantic feature vector through a classifier-based traffic light controller to obtain a control instruction, where the control instruction is used to indicate whether to extend the green light time.

[0089] Specifically, the green light time control module 860 is used to: use the fully connected layer of the classifier-based traffic light controller to fully connect the pedestrian flow temporal reasoning semantic feature vector to obtain an encoded classification feature vector; and input the encoded classification feature vector into the Softmax classification function of the classifier-based traffic light controller to obtain the control instruction.

[0090] It should be understood that the role of a classifier is to use given categories and known training data to learn classification rules and classifiers, and then classify (or predict) unknown data. Logistic regression and SVM are commonly used to solve binary classification problems. For multi-class classification problems, logistic regression or SVM can also be used, but multiple binary classifications are required to form a multi-class classification. However, this is prone to errors and is inefficient. A commonly used multi-classification method is the Softmax classification function.

[0091] Furthermore, in the technical solution disclosed herein, the intelligent traffic diversion system for people and vehicles in scenic areas also includes a training module for training the crowd distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale intensive retention attention module, the crowd flow temporal distribution inference module and the classifier-based traffic light controller.

[0092] Wherein, the training module includes: a training data acquisition unit for acquiring training data, wherein the training data includes a training crowd flow monitoring video collected by a camera deployed at a scenic spot intersection; a training crowd flow monitoring key frame sampling unit for performing discrete key frame sampling on the training crowd flow monitoring video to obtain a time series of training crowd flow monitoring key frames; a training crowd flow distribution semantic feature extraction unit for performing feature extraction on each training crowd flow monitoring key frame in the time series of the training crowd flow monitoring key frames through the crowd flow distribution feature extractor based on the convolutional neural network model to obtain a time series of training crowd flow distribution semantic feature maps; a training crowd flow distribution semantic space feature attention unit for performing feature extraction on the time series of the training crowd flow distribution semantic feature maps through the spatial information multi-scale dense retention attention module to obtain a training space feature map. A time series of spatially significant semantic feature maps of pedestrian distribution is presented; a training pedestrian temporal reasoning unit is used to pass the time series of the training spatially significant semantic feature maps of pedestrian distribution through the pedestrian temporal distribution reasoning module to obtain a training pedestrian temporal reasoning semantic feature vector; a loss calculation unit is used to pass the training pedestrian temporal reasoning semantic feature vector through the classifier-based traffic light controller to obtain a classification loss function value; and a loss training unit is used to train the pedestrian distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale dense retention attention module, the pedestrian temporal distribution reasoning module and the classifier-based traffic light controller based on the classification loss function value, wherein, in each round of iteration of the training, the training pedestrian temporal reasoning semantic feature vector is corrected.

[0093] In the above technical solution, the time series of the training crowd flow distribution semantic feature map represents the image semantic features of each training crowd flow monitoring key frame, and after the time series of the training crowd flow distribution semantic feature map passes through the spatial information multi-scale dense retention attention module and the crowd flow temporal distribution inference module, the temporal distribution pattern inference between feature matrices can be performed based on the image semantic spatial distribution multi-scale dense attention enhancement of the image semantic features of the local training crowd flow monitoring key frame in the feature matrix. Therefore, the training crowd flow temporal reasoning semantic feature vector will have the temporal-spatial cross-dimensional correlation feature expression complexity of the multi-scale correlation features under the temporal-spatial dimension, that is, the training crowd flow temporal reasoning semantic feature vector has rich geometric shape details in the high-dimensional feature space, so that there is category representation distortion when it is classified by the classifier to map to the class probability domain, that is, it affects the category representation of the training crowd flow temporal reasoning semantic feature vector for the classifier's predetermined category, thereby reducing the accuracy of the classification result.

[0094] Based on this, the applicant of the present application corrects the training pedestrian flow temporal reasoning semantic feature vector each time the training pedestrian flow temporal reasoning semantic feature vector is classified and iterated through a classifier-based traffic light controller.

[0095] Accordingly, in one example, in each iteration of the training, the training pedestrian flow temporal reasoning semantic feature vector is corrected using the following correction formula to obtain a corrected training pedestrian flow temporal reasoning semantic feature vector; wherein the correction formula is:

[0096]

[0097] Wherein, V is the semantic feature vector of the training pedestrian flow temporal reasoning, p is the class probability value obtained by the traffic light controller based on the classifier for the semantic feature vector V of the training pedestrian flow temporal reasoning, and v i is the i-th eigenvalue of the semantic feature vector V for the training pedestrian flow temporal reasoning, is the mean of all eigenvalues ​​of the semantic feature vector V for temporal reasoning of human traffic flow, ‖V‖1 represents the norm of the semantic feature vector V for temporal reasoning of human traffic flow flow, and α is a weight hyperparameter. exp(·) represents the exponential operation of a value, and the exponential operation of the value represents the calculation of the natural exponential function value with the value as the power. v i ′ is the i-th eigenvalue of the corrected training pedestrian flow temporal reasoning semantic feature vector.

[0098] Specifically, while maintaining the geometric shape details of the high-dimensional feature manifold under the class probability mapping of the training pedestrian flow temporal reasoning semantic feature vector V, the shape attribute editing of the feature value details relative to the variational representation of the feature set of the training pedestrian flow temporal reasoning semantic feature vector V is performed through the distortion consultation of the class probability, thereby mapping the class probability distortion of the training pedestrian flow temporal reasoning semantic feature vector V to the latent class space feature representation, and then performing consultation fusion by supplementing the basic low-rank constrained latent inversion representation to bridge the gap between the edited consultation representation and the original geometric shape details, thereby improving the category representation of the high-dimensional features of the training pedestrian flow temporal reasoning semantic feature vector V, thereby improving the accuracy of the classification results. In this way, the temporal pattern of pedestrian flow distribution can be more accurately inferred based on the temporal-spatial dimension characteristics of pedestrian flow distribution, and the next pedestrian flow situation can be predicted, so that the green light time can be adjusted intelligently, the operating efficiency of the scenic area and the tourist experience can be improved, and the probability of accidents can be reduced.

[0099] Based on the above embodiments, see Figure 5 As shown in FIG. 1 , a flow chart of a method for intelligently directing people and vehicles in a scenic area according to an embodiment of the present disclosure is shown. For example, the method for intelligently directing people and vehicles in a scenic area can be executed by a server, which can be Figure 1 The server 100 shown in FIG. Figure 5 As shown, the method for intelligently guiding personnel and vehicles in a scenic area according to an embodiment of the present disclosure includes the following steps: S510, obtaining a crowd monitoring video collected by a camera deployed at a scenic intersection; S520, performing discrete sampling of key frames on the crowd monitoring video to obtain a time series of crowd monitoring key frames; S530, performing feature extraction on each crowd monitoring key frame in the time series of the crowd monitoring key frames through a crowd distribution feature extractor based on a deep neural network model to obtain a time series of crowd distribution semantic feature maps; S540, passing the time series of the crowd distribution semantic feature maps through a spatial information multi-scale dense retention attention module to obtain a time series of spatially significant crowd distribution semantic feature maps; S550, passing the time series of the spatially significant crowd distribution semantic feature maps through a crowd temporal distribution inference module to obtain a crowd temporal inference semantic feature vector as a crowd temporal inference semantic feature; and, S560, determining whether to extend the green light time based on the crowd temporal inference semantic feature.

[0100] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned method for intelligently guiding the flow of people and vehicles in scenic spots have been described in detail in the above reference. Figures 2 to 4 It has been introduced in detail in the description of the scenic area personnel and vehicle intelligent diversion system 800, and therefore, its repeated description will be omitted.

[0101] Figure 6 This is an application scenario diagram of the intelligent diversion system for scenic area personnel and vehicles according to the embodiment of the present disclosure. Figure 6 As shown, in this application scenario, first, a pedestrian flow monitoring video collected by a camera deployed at a scenic spot intersection is obtained (for example, Figure 6 Then, the crowd flow monitoring video is input to a server (for example, Figure 6 In S) shown in , the server can use the scenic area personnel and vehicle intelligent diversion algorithm to process the crowd flow monitoring video to obtain a control instruction for indicating whether to extend the green light time.

[0102] Based on the above embodiments, another exemplary embodiment of an electronic device is provided in the present disclosure. In some possible implementations, the electronic device in the present disclosure may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for intelligently directing people and vehicles in a scenic area are implemented in the above embodiments.

[0103] For example, the electronic device is disclosed Figure 1 Taking the server 100 in the example for explanation, the processor in the electronic device is the processor 110 in the server 100, and the memory in the electronic device is the memory 120 in the server 100.

[0104] An embodiment of the present disclosure also provides a computer-readable storage medium. Figure 7 1 shows a schematic diagram of a computer-readable storage medium 1000 according to an embodiment of the present disclosure. Figure 7 As shown, the computer-readable storage medium 1000 stores computer-executable instructions 1001. When the computer-executable instructions 1001 are executed by the processor, the method for intelligently guiding personnel and vehicles in a scenic area according to an embodiment of the present disclosure described with reference to the above figures can be executed. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0105] Embodiments of the present disclosure also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the method for intelligently directing people and vehicles in a scenic area according to an embodiment of the present disclosure.

[0106] Those skilled in the art will appreciate that the contents disclosed in this disclosure may be subject to various modifications and improvements. For example, the various devices or components described above may be implemented through hardware, software, firmware, or a combination of some or all of the three.

[0107] In addition, although the present disclosure makes various references to certain units in the system according to embodiments of the present disclosure, any number of different units can be used and run on the client and / or server. The units are only illustrative, and different aspects of the systems and methods can use different units.

[0108] Those skilled in the art will appreciate that all or part of the steps in the above method can be performed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software functional modules. The present disclosure is not limited to any particular combination of hardware and software.

[0109] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized or highly formal sense, unless explicitly defined as such herein.

[0110] The above is an illustration of the present disclosure and should not be considered as a limitation thereof. Although several exemplary embodiments of the present disclosure have been described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present disclosure. Therefore, all such modifications are intended to be included within the scope of the present disclosure as defined by the claims. It should be understood that the above is an illustration of the present disclosure and should not be considered as limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present disclosure is defined by the claims and their equivalents.

Claims

1. A smart diversion system for people and vehicles in scenic spots, characterized by: include: The crowd flow monitoring video acquisition module is used to obtain crowd flow monitoring videos collected by cameras deployed at intersections in scenic areas; A crowd flow monitoring key frame sampling module is used to perform discrete key frame sampling on the crowd flow monitoring video to obtain a time series of crowd flow monitoring key frames; A crowd distribution semantic feature extraction module is used to extract features from each crowd flow monitoring key frame in the time series of the crowd flow monitoring key frames using a crowd distribution feature extractor based on a deep neural network model to obtain a time series of crowd flow distribution semantic feature maps; A crowd flow distribution semantic spatial feature attention module is used to pass the time series of the crowd flow distribution semantic feature map through a spatial information multi-scale intensive retention attention module to obtain a time series of spatially significant crowd flow distribution semantic feature maps; A pedestrian flow temporal reasoning module is used to pass the time series of the spatially significant pedestrian flow distribution semantic feature map through the pedestrian flow temporal distribution reasoning module to obtain a pedestrian flow temporal reasoning semantic feature vector as a pedestrian flow temporal reasoning semantic feature; as well as A green light time control module is used to determine whether to extend the green light time based on the pedestrian flow temporal reasoning semantic features; Among them, the crowd flow time series reasoning module includes: a vectorization expansion unit, which is used to vectorize and expand multiple spatially significant crowd flow distribution semantic feature maps in the time series of the spatially significant crowd flow distribution semantic feature map to obtain a sequence of spatially significant crowd flow distribution semantic feature vectors; a vector screening unit, which is used to determine the spatially significant crowd flow distribution semantic feature vector of the last position in the sequence of the spatially significant crowd flow distribution semantic feature vectors; a crowd flow distribution average information difference calculation unit, which is used to calculate the positional difference between the mean feature vector of multiple spatially significant crowd flow distribution semantic feature vectors in the sequence of the spatially significant crowd flow distribution semantic feature vectors and the spatially significant crowd flow distribution semantic feature vector of the last position to obtain the crowd flow distribution average information difference feature vector; a crowd flow distribution random information difference calculation unit, which is used to calculate the spatially significant crowd flow distribution semantic feature vector. The method comprises the following steps: a first crowd distribution inference unit is configured to multiply the crowd distribution average information difference feature vector by a first predetermined weight hyperparameter and a first Gaussian distribution random number function value to obtain a first crowd distribution inference feature vector; a second crowd distribution inference unit is configured to multiply the crowd distribution random information difference feature vector by a second predetermined weight hyperparameter and a second Gaussian distribution random number function value to obtain a second crowd distribution inference feature vector; and a fusion unit is configured to calculate the positional addition between the first crowd distribution inference feature vector and the second crowd distribution inference feature vector to obtain the crowd flow temporal inference semantic feature vector.

2. The intelligent diversion system for people and vehicles in scenic spots according to claim 1 is characterized in that: The deep neural network model is a convolutional neural network model.

3. The intelligent diversion system for people and vehicles in scenic spots according to claim 2 is characterized in that: The crowd flow distribution semantic space feature attention module includes: A crowd distribution semantic feature map multi-mode pooling unit is used to perform multi-mode pooling processing on each crowd distribution semantic feature map in the time series of the crowd distribution semantic feature map along the channel dimension to obtain a spliced ​​crowd distribution semantic feature matrix; A crowd flow distribution implicit correlation feature capture unit, configured to perform convolution processing on the spliced ​​crowd flow distribution semantic feature matrix through a convolution layer to obtain a crowd flow distribution implicit correlation feature matrix; a crowd flow distribution spatial attention weight calculation activation unit, configured to input the crowd flow distribution implicit correlation feature matrix into a Sigmoid activation unit to obtain a crowd flow distribution spatial attention weight matrix; and The spatial saliency enhancement unit is used to multiply each feature matrix along the channel dimension in the crowd distribution semantic feature map by the crowd distribution spatial attention weight matrix at each position point to obtain the spatially salient crowd distribution semantic feature map.

4. The intelligent diversion system for people and vehicles in scenic spots according to claim 3 is characterized in that: The crowd flow distribution semantic feature map multi-mode pooling unit includes: A global mean pooling subunit, configured to perform global mean pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic global mean pooling feature matrix; A maximum pooling subunit is used to perform maximum pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic maximum pooling feature matrix; a random pooling subunit, configured to perform random pooling processing on the crowd flow distribution semantic feature map along the channel dimension to obtain a crowd flow distribution semantic random pooling feature matrix; and A multi-pooling semantic splicing unit is used to splice the crowd flow distribution semantic mean pooling feature matrix, the crowd flow distribution semantic maximum pooling feature matrix and the crowd flow distribution semantic random pooling feature matrix to obtain the spliced ​​crowd flow distribution semantic feature matrix.

5. The intelligent diversion system for people and vehicles in scenic spots according to claim 4 is characterized in that: The first Gaussian distribution random number function value and the second Gaussian distribution random number function value are generated by a Gaussian distribution random number function with a mean of 0 and a variance of 1.

6. The intelligent diversion system for people and vehicles in scenic spots according to claim 5 is characterized in that: The green light time control module is used to: The pedestrian flow temporal reasoning semantic feature vector is passed through a traffic light controller based on a classifier to obtain a control instruction, where the control instruction is used to indicate whether to extend the green light time.

7. The intelligent diversion system for people and vehicles in scenic spots according to claim 6 is characterized in that: It also includes a training module for training the crowd distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale intensive retention attention module, the crowd flow temporal distribution reasoning module and the classifier-based traffic light controller.

8. The intelligent diversion system for people and vehicles in scenic spots according to claim 7 is characterized in that: The training module includes: A training data acquisition unit, configured to acquire training data, wherein the training data includes training pedestrian flow monitoring videos collected by cameras deployed at intersections in scenic areas; A training crowd flow monitoring key frame sampling unit is used to perform key frame discrete sampling on the training crowd flow monitoring video to obtain a time series of training crowd flow monitoring key frames; A training crowd flow distribution semantic feature extraction unit is used to extract features from each training crowd flow monitoring key frame in the time series of the training crowd flow monitoring key frames through the crowd flow distribution feature extractor based on the convolutional neural network model to obtain a time series of training crowd flow distribution semantic feature maps; A training crowd flow distribution semantic space feature attention unit is used to pass the time series of the training crowd flow distribution semantic feature map through the spatial information multi-scale dense retention attention module to obtain a time series of training spatially significant crowd flow distribution semantic feature map; A training pedestrian flow temporal reasoning unit is used to pass the time series of the training spatially significant pedestrian flow distribution semantic feature map through the pedestrian flow temporal distribution reasoning module to obtain a training pedestrian flow temporal reasoning semantic feature vector; a loss calculation unit, configured to pass the training pedestrian flow temporal reasoning semantic feature vector through the classifier-based traffic light controller to obtain a classification loss function value; and A loss training unit is used to train the crowd distribution feature extractor based on the convolutional neural network model, the spatial information multi-scale dense retention attention module, the crowd flow temporal distribution reasoning module and the classifier-based traffic light controller based on the classification loss function value, wherein in each round of training iteration, the training crowd flow temporal reasoning semantic feature vector is corrected.

9. A method for intelligently guiding people and vehicles in a scenic area, using the intelligent guiding system for people and vehicles in a scenic area according to claim 1, characterized in that: include: Obtain pedestrian flow monitoring videos collected by cameras deployed at intersections in scenic areas; Performing discrete key frame sampling on the crowd flow monitoring video to obtain a time series of crowd flow monitoring key frames; Performing feature extraction on each of the crowd flow monitoring key frames in the time series of the crowd flow monitoring key frames using a crowd flow distribution feature extractor based on a deep neural network model to obtain a time series of crowd flow distribution semantic feature maps; The time series of the crowd flow distribution semantic feature map is passed through a spatial information multi-scale dense retention attention module to obtain a time series of spatially significant crowd flow distribution semantic feature map; The time series of the spatially significant crowd flow distribution semantic feature graph is passed through a crowd flow temporal distribution reasoning module to obtain a crowd flow temporal reasoning semantic feature vector as a crowd flow temporal reasoning semantic feature; as well as Based on the pedestrian flow temporal reasoning semantic features, it is determined whether to extend the green light time.

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