Noise vehicle monitoring method and device, electronic equipment, storage medium and product

Abstract

The application discloses a noise vehicle monitoring method and device, electronic equipment, storage medium and product. The method comprises the following steps: in response to a noise vehicle monitoring event being triggered, monitoring images of each point of a target road are acquired within a preset time period, wherein at least one of the monitoring images contains a building image; each building image is analyzed to determine the number of continuously lighted layers of a sound control lamp in a staircase of a target building corresponding to the building image; and when the number of continuously lighted layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that there is a noise vehicle on the target road. According to the technical scheme provided by the embodiment of the application, without adding a sound detection device, the number of continuously lighted layers of a sound control lamp in a staircase of a building beside a road is analyzed, and whether there is a noise vehicle in the road can be monitored in real time.

Description

Technical Field

[0001] This invention relates to the field of video surveillance technology, and in particular to a method, device, electronic equipment, storage medium, and product for monitoring noisy vehicles. Background Technology

[0002] With the continuous improvement of economic level, the craze for modifying cars and motorcycles has gradually taken off. After vehicle modification, noise often increases, which causes great trouble to residents along the street, especially when the vehicle is driving at night.

[0003] Currently, traffic police are the main body responsible for cracking down on vehicles that generate a lot of noise. However, this approach not only requires a large number of police officers but also cannot solve the problem of noise vehicles disturbing residents along the street in a timely manner. Summary of the Invention

[0004] This invention provides a method, device, electronic device, storage medium, and product for monitoring noisy vehicles, which can monitor the presence of noisy vehicles on the road in real time.

[0005] According to one aspect of the present invention, a method for monitoring noisy vehicles is provided, comprising:

[0006] In response to a noise vehicle monitoring event being triggered, monitoring images taken at various points on the target road are acquired within a preset time period, wherein at least one of the monitoring images contains a building image;

[0007] Each building image is analyzed to determine the number of consecutive floors where the voice-activated lights in the stairwell of the target building corresponding to the building image are lit.

[0008] When the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that there are noisy vehicles on the target road.

[0009] According to another aspect of the present invention, a noise vehicle monitoring device is provided, comprising:

[0010] The building image acquisition module is used to acquire monitoring images taken at various points on the target road within a preset time period in response to the triggering of a noise vehicle monitoring event, wherein at least one of the monitoring images contains a building image.

[0011] The module for determining the number of consecutively lit layers is used to analyze each of the building images and determine the number of consecutively lit layers of the voice-activated lights in the stairwell of the target building corresponding to the building image.

[0012] The noise vehicle monitoring module is used to determine that there are noisy vehicles on the target road when the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the noise vehicle monitoring method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the noise vehicle monitoring method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the noise vehicle monitoring method according to any embodiment of the present invention.

[0019] The noise vehicle monitoring scheme of this invention, in response to a noise vehicle monitoring event being triggered, acquires monitoring images taken at various points along a target road within a preset time period, wherein at least one of the monitoring images includes a building image; analyzes each building image to determine the number of consecutively lit layers of the motion-activated lights in the stairwells of the target building corresponding to that building image; when the number of consecutively lit layers corresponding to at least one building image is greater than a preset threshold, it is determined that a noise vehicle exists on the target road. Through the technical solution provided by this invention, without the need for additional sound detection devices, the presence of noise vehicles on the road can be monitored in real time by analyzing the number of consecutively lit layers of motion-activated lights in the stairwells of buildings along the road.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a noise vehicle monitoring method provided in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of the monitoring screen corresponding to the location setting of a smart camera on a target road, provided in an embodiment of the present invention;

[0024] Figures 3a-3d These are schematic diagrams of building images provided in embodiments of the present invention;

[0025] Figure 4 A flowchart of a noise vehicle monitoring method provided in an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of a panoramic camera monitoring screen corresponding to a point on a target road, provided as an embodiment of the present invention;

[0027] Figures 6a-6c These are images of vehicles traveling on the target road, captured by three different monitoring points working together.

[0028] Figure 7 This is a schematic diagram of the structure of a noise vehicle monitoring device provided in an embodiment of the present invention;

[0029] Figure 8 A schematic diagram of the structure of an electronic device for implementing the noise vehicle monitoring method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Figure 1 This is a flowchart illustrating a method for monitoring noisy vehicles according to an embodiment of the present invention. This embodiment is applicable to situations requiring real-time monitoring of noisy vehicles traveling on roads. The method can be executed by a noisy vehicle monitoring device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110. In response to the triggering of a noise vehicle monitoring event, monitoring images taken at various points on the target road are acquired within a preset time period, wherein at least one of the monitoring images contains a building image.

[0034] In this embodiment of the invention, a noise vehicle monitoring event is determined to be triggered when a user inputs a noise vehicle monitoring command. Optionally, a noise vehicle monitoring time period can be preset, such as setting 20:00-24:00 and 5:00-8:00 every day as the noise vehicle monitoring time period. When the time falls within this noise vehicle monitoring time period, the noise vehicle monitoring event is automatically and periodically triggered. In response to the triggering of the noise vehicle monitoring event, monitoring images taken at various points on the target road are acquired within the preset time period, wherein at least one monitoring image includes a building image. Specifically, the various points can be set sequentially along the vehicle travel direction on the target road, and each point is equipped with a smart camera. The smart camera can not only capture images of the target road, but also images of the corresponding buildings next to the target road (i.e., within a preset distance range of the target road). The monitoring image of the smart camera can cover only the stairwell area of ​​the building, or it can cover the entire building. It should be noted that this explanation pertains to the fact that the coverage area of ​​the monitoring images from the smart cameras at each location is not limited in this embodiment of the invention, as long as at least one of the monitoring images captured by each location contains an image of a building. For example, Figure 2This is a schematic diagram of a monitoring screen corresponding to a smart camera positioned at a target road, provided as an embodiment of the present invention. For example... Figure 2 As shown, the monitoring footage from the smart cameras positioned at the target road locations can cover [the area]. Figure 2 The entire building is within the red box. Optionally, the target road locations correspond one-to-one with the buildings; that is, to improve the accuracy of noise and vehicle monitoring, one location monitors one building.

[0035] S120. Analyze each of the building images to determine the number of consecutively lit floors of the voice-activated lights in the stairwells of the target building corresponding to the building image.

[0036] In this embodiment of the invention, each stairwell in a building is equipped with an adjustable sound-activated light. In response to a noise vehicle monitoring event, the sensitivity of the adjustable sound-activated light in each stairwell is adjusted. The adjusted threshold is higher than the original threshold; that is, the threshold for the adjustable sound-activated light in each stairwell of the building used for noise vehicle monitoring is higher than the threshold for adjustable sound-activated lights used for general lighting. For example, the threshold values ​​for the adjustable sound-activated lights corresponding to different noise vehicle monitoring periods can be the same or different. For instance, for the nighttime noise vehicle monitoring period from 8:00 PM to midnight, the threshold value can be set to 55 decibels; for the daytime noise vehicle monitoring period from 5:00 AM to 8:00 AM, the threshold value can be set to 70 decibels. When a noisy vehicle travels on the target road, it generates significant noise. When the vehicle noise exceeds the threshold value of the sound-activated light in the stairwell, the light will illuminate. Therefore, by analyzing building images in surveillance footage, the number of consecutively lit layers of the motion-activated lights in the stairwells of the target building corresponding to the building image is determined. For example, the building image can be input into a pre-trained model for determining the number of consecutively lit layers. The output of this model determines the number of consecutively lit layers of the motion-activated lights in the stairwells of the target building corresponding to the building image. Here, the model for determining the number of consecutively lit layers is a pre-built machine learning model.

[0037] Optionally, each of the building images is analyzed separately to determine the number of consecutively lit floors of the voice-activated lights in the stairwells of the target building corresponding to the building image. This includes: for each building image, traversing the window areas of each floor of the stairwell in the building image in ascending order of floor level, determining whether the voice-activated lights in each floor of the target building corresponding to the building image are lit based on the window areas of the stairwells; and determining the number of consecutively lit floors of the voice-activated lights in the stairwells of the target building corresponding to the building image based on the determination results. For example, for each building image, stairwell window recognition is performed to identify the stairwell window areas on each floor of the building image. Then, the stairwell window areas on each floor of the building image are traversed in ascending order of floor level. The brightness of the stairwell window areas is detected, and it is determined whether the brightness of the stairwell window area is greater than a preset brightness threshold. If so, the motion-activated light of the stairwell corresponding to that floor is determined to be lit. The brightness of the stairwell window area on the floor above that floor is then detected, until the brightness of the stairwell window area is found to be less than the preset brightness threshold. At this point, the number of floors where the brightness of the stairwell window area is greater than the preset brightness threshold is counted, and this number is taken as the number of consecutive lit floors for the motion-activated light of the stairwell in the target building corresponding to the building image. For example, Figures 3a-3d These are schematic diagrams of building images provided in embodiments of the present invention. Figure 3a The number of floors where the motion-activated lights in the stairwells of the target building corresponding to the building image are continuously lit is 4. Figure 3b The number of floors where the motion-activated lights in the stairwells of the target building corresponding to the building image are continuously lit is 6. Figure 3c The number of consecutively lit floors for the motion-activated lights in the stairwells of the target building corresponding to the building image is 0. Figure 3d The number of consecutive lit floors for the motion-activated lights in the stairwells of the target building corresponding to the building image is 2.

[0038] Optionally, before determining the number of consecutively lit stairwell sound-activated lights in the target building corresponding to the building image based on the judgment result, the method further includes: performing damage detection on the stairwell sound-activated lights in the target building corresponding to the building image; determining the number of consecutively lit stairwell sound-activated lights in the target building corresponding to the building image based on the judgment result, including: determining the number of consecutively lit stairwell sound-activated lights in the target building corresponding to the building image based on the damage detection result and the judgment result. Specifically, the continuous unlit duration of the stairwell sound-activated lights in the target building corresponding to the building image is determined. If the continuous unlit duration of some stairwell sound-activated lights exceeds a preset duration, such as 3 days, then it is determined that the sound-activated lights in that stairwell are damaged. Therefore, when determining the number of consecutively lit stairwell sound-activated lights in the target building, damaged sound-activated lights are excluded. That is, "continuous" lighting refers to normal stairwell sound-activated lights, without considering damaged sound-activated lights.

[0039] S130. When the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that there are noisy vehicles on the target road.

[0040] In this embodiment of the invention, for each building image, the number of consecutively lit layers is determined to be greater than a preset layer threshold. If at least one building image has a number of consecutively lit layers greater than the preset layer threshold, it is determined that there are noisy vehicles on the target road. If the number of consecutively lit layers for all building images is less than the preset layer threshold, it is determined that there are no noisy vehicles on the target road.

[0041] The noise vehicle monitoring method of this invention, in response to a noise vehicle monitoring event being triggered, acquires monitoring images taken at various points along a target road within a preset time period, wherein at least one of the monitoring images includes a building image; analyzes each building image to determine the number of consecutively lit layers of the motion-activated lights in the stairwells of the target building corresponding to the building image; when the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that a noise vehicle exists on the target road. Through the technical solution provided by this invention, without the need for additional sound detection devices, the presence of noise vehicles on the road can be monitored in real time by analyzing the number of consecutively lit layers of motion-activated lights in the stairwells of buildings along the road.

[0042] In some embodiments, after determining that a noisy vehicle exists on the target road, the method further includes: issuing a vehicle noise alarm; wherein, the larger the number of consecutively illuminated layers, the higher the alarm level. Specifically, when the number of consecutively illuminated layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that a noisy vehicle exists on the target road. At this time, a vehicle noise alarm is issued, wherein, the larger the number of consecutively illuminated layers, the higher the alarm level. For example, a vehicle noise alarm method with alarm levels in ascending order may include: issuing a warning via voice broadcast; issuing a warning via voice broadcast and pushing the captured noisy vehicle information to the traffic police processing terminal, so that the traffic police can criticize and educate the corresponding noisy vehicle owner based on the noisy vehicle information received by the traffic police processing terminal; and imposing penalties such as fines and demerit points on the captured noisy vehicle owner.

[0043] Figure 4 A flowchart of a noise vehicle monitoring method provided in an embodiment of the present invention is shown below. Figure 4 As shown, the method includes:

[0044] S410. In response to the triggering of a noise vehicle monitoring event, monitoring images taken at various points on the target road are acquired within a preset time period, wherein at least one of the monitoring images contains a building image.

[0045] S420. Analyze each of the building images to determine the number of consecutively lit floors of the voice-activated lights in the stairwells of the target building corresponding to the building image.

[0046] S430. When the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that there are noisy vehicles on the target road.

[0047] S440. Acquire images of vehicles traveling on the target road simultaneously with the images of each target building; wherein, the target building images are the building images with a continuous number of illuminated layers greater than the preset layer threshold.

[0048] In this embodiment of the invention, a panoramic camera corresponding to a predetermined location on the road is also installed. While the smart camera at the target road location captures images of the corresponding buildings beside the target road, the panoramic camera simultaneously captures images of vehicles traveling on the target road. For example, Figure 5 This is a schematic diagram of a panoramic camera monitoring screen corresponding to a point on a target road, provided as an embodiment of the present invention. For example... Figure 5 As shown, the panoramic camera's monitoring image can cover vehicles traveling within a certain driving range on the target road. Figure 5 (Vehicles within the red box in the image). Since there may be multiple buildings along the target road in the direction of vehicle travel, monitoring points can be set sequentially for each building in the direction of vehicle travel. Since vehicles are constantly traveling along the target road, images of each target building with a consecutive illuminated layer number greater than a preset layer number threshold are identified within the target time period. Images of vehicles traveling on the target road are also acquired simultaneously with the panoramic camera images of each target building.

[0049] S450. Determine noisy vehicles on the target road based on each of the images of the driving vehicles.

[0050] In this embodiment of the invention, noisy vehicles on a target road are determined based on images of various vehicles in motion. Generally, noisy vehicles are more likely to appear on relatively empty roads; if there is only one vehicle in an image, that vehicle can be identified as a noisy vehicle. This embodiment of the invention provides a technical solution for determining noisy vehicles when multiple vehicles are present in an image: Optionally, determining noisy vehicles on the target road based on images of various vehicles in motion includes: comparing the images of various vehicles in motion, determining the target vehicle with the highest frequency of occurrence in the images of various vehicles in motion, and identifying the target vehicle as a noisy vehicle on the target road. Specifically, comparing the images of various vehicles in motion, determining the number of times the same license plate appears in all images of various vehicles in motion, and taking the number of times the same license plate appears in all images of various vehicles in motion as the frequency of occurrence of the corresponding vehicle in the images of various vehicles in motion. The target vehicle corresponding to the license plate with the highest frequency of occurrence is identified as a noisy vehicle on the target road. For example, Figures 6a-6c These are images of vehicles traveling on the target road, captured in a coordinated manner from three different monitoring points. According to 6a- Figure 6c It can be seen that the frequency of blue, red, and yellow vehicles is 2, while the frequency of green vehicles is 3. Obviously, green vehicles appear most frequently in all images of moving vehicles. Therefore, green vehicles are considered as noise vehicles on the target road. Since noise vehicles generally travel at higher speeds, they create speed and distance differences with normal-moving vehicles, causing them to appear more frequently in the images triggered by the corridor light detection.

[0051] Optionally, determining the noisy vehicles on the target road based on each of the driving vehicle images includes: determining the vehicle model of each vehicle included in each driving vehicle image; searching in a preset noisy vehicle image library for vehicle images matching the vehicle models; determining the noisy vehicle confidence score for each vehicle included in each driving vehicle image based on the search results; and determining the noisy vehicles on the target road from the vehicles included in each driving vehicle image based on the noisy vehicle confidence score. For example, each driving vehicle image is analyzed based on a preset vehicle model recognition algorithm to determine the vehicle model of each vehicle included in the driving vehicle image; a preset noisy vehicle image library is searched for vehicle images matching the vehicle models of each vehicle included in the driving vehicle image; and the noisy vehicle confidence score for each vehicle included in the driving vehicle image is determined based on the search results. The preset noisy vehicle image library includes images of vehicle models corresponding to vehicles with a high probability of modification, such as BMW M4, Mercedes-AMG, Mitsubishi Lancer EX, and various motorcycles. Optionally, if no vehicle image matching the vehicle model is found in the preset noisy vehicle image library, the noise vehicle confidence score of the vehicle corresponding to that vehicle model in the driving vehicle image is determined to be 0. If a vehicle image matching the vehicle model is found in the preset noisy vehicle image library, the frequency of occurrence of that vehicle model in all driving vehicle images is further determined, and the noise vehicle confidence score of the vehicle corresponding to that vehicle model in the driving vehicle image is determined based on the frequency of occurrence, wherein the noise vehicle confidence score is directly proportional to the frequency of occurrence of that vehicle. Based on the noise vehicle confidence score, noisy vehicles on the target road are determined from all vehicles included in all driving vehicle images. For example, the vehicle with the highest noise vehicle confidence score can be used as a noisy vehicle on the target road, or vehicles with a noise vehicle confidence score greater than a preset confidence threshold can be used as noisy vehicles on the target road. There can be one or more noisy vehicles.

[0052] S460, Issue a vehicle noise alarm for the noisy vehicle.

[0053] In this embodiment of the invention, a vehicle noise alarm is issued to a noisy vehicle. For example, the noisy vehicle can be warned in the form of a voice broadcast, or a noise alarm can be sent to the driver's terminal (such as a mobile phone) corresponding to the noisy vehicle. The noise alarm may include warnings, demerit points, or violation penalties.

[0054] The noise vehicle monitoring method of this invention can monitor the presence of noisy vehicles on the road in real time by analyzing the number of consecutive lit layers of sound-activated lights in the stairwells of buildings along the road, without the need for additional sound detection devices. It can also identify the noisy vehicles generating noise based on captured images of moving vehicles and issue noise alarms to them in a timely manner.

[0055] Figure 7This is a schematic diagram of a noise vehicle monitoring device provided in an embodiment of the present invention. Figure 7 As shown, the device includes:

[0056] The building image acquisition module 710 is used to acquire monitoring images taken at various points on the target road within a preset time period in response to the triggering of a noise vehicle monitoring event, wherein at least one of the monitoring images contains a building image.

[0057] The continuous illumination layer determination module 720 is used to analyze each of the building images and determine the continuous illumination layer of the voice-activated lights in the stairwell of the target building corresponding to the building image.

[0058] The noise vehicle monitoring module 730 is used to determine that there are noisy vehicles on the target road when the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold.

[0059] Optionally, the device further includes:

[0060] The alarm module is used to issue a vehicle noise alarm after determining that there is a noisy vehicle on the target road; wherein, the greater the number of consecutively lit layers, the higher the alarm level.

[0061] Optionally, the device further includes:

[0062] The vehicle image acquisition module is used to acquire vehicle images on the target road simultaneously with images of each target building before issuing a vehicle noise alarm; wherein, the target building images are building images with a consecutive number of illuminated layers greater than the preset number of layers threshold.

[0063] A noise vehicle determination module is used to determine noisy vehicles on the target road based on each of the images of the driving vehicles.

[0064] The alarm module is used for:

[0065] Issue a vehicle noise alarm for the noisy vehicle.

[0066] Optionally, the noise vehicle determination module is used for:

[0067] The images of each of the vehicles are compared to identify the target vehicle that appears most frequently in the images, and this target vehicle is identified as a noise vehicle on the target road.

[0068] Optionally, the noise vehicle determination module is used for:

[0069] Each of the images of the driving vehicles contains the vehicle model;

[0070] Search the preset noisy vehicle image library to see if there is a vehicle image that matches the vehicle model, and determine the noisy vehicle confidence level of each vehicle in each driving vehicle image based on the search results;

[0071] Based on the noise vehicle confidence level, the noisy vehicles on the target road are determined from the individual vehicles contained in each of the driving vehicle images.

[0072] Optionally, the module for determining the number of consecutively lit layers is used to:

[0073] For each building image, the window area of ​​each stairwell in the building image is traversed in order from low to high. Based on the window area of ​​the stairwell, it is determined whether the voice-activated lights in each stairwell of the target building corresponding to the building image are lit.

[0074] Based on the judgment result, determine the number of consecutive floors where the voice-activated lights in the stairwells of the target building corresponding to the building image are lit.

[0075] The noise vehicle monitoring device provided in this embodiment of the invention can execute the noise vehicle monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0076] Figure 8 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0077] like Figure 8As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0078] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0079] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as noise vehicle monitoring methods.

[0080] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0081] In some embodiments, the noise vehicle monitoring method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the noise vehicle monitoring method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the noise vehicle monitoring method by any other suitable means (e.g., by means of firmware).

[0082] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0083] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0084] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0085] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0086] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0087] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0088] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0089] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for monitoring noisy vehicles, characterized in that, include: In response to a noise vehicle monitoring event being triggered, monitoring images taken at various points on the target road are acquired within a preset time period, wherein at least one of the monitoring images contains a building image; Each building image is analyzed to determine the number of consecutive floors where the voice-activated lights in the stairwell of the target building corresponding to the building image are lit. When the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold, it is determined that there are noisy vehicles on the target road.

2. The method according to claim 1, characterized in that, After determining that noisy vehicles exist on the target road, the process also includes: The vehicle noise alarm is triggered; the greater the number of consecutively illuminated layers, the higher the alarm level.

3. The method according to claim 2, characterized in that, Before issuing a vehicle noise warning, the following is also included: Images of vehicles traveling on the target road are captured simultaneously with images of each target building; wherein, the target building images are building images with a consecutive number of illuminated layers greater than the preset layer threshold. Noisy vehicles on the target road are identified based on the images of each of the driving vehicles. Issue vehicle noise warnings, including: Issue a vehicle noise alarm for the noisy vehicle.

4. The method according to claim 3, characterized in that, Determining noisy vehicles on the target road based on each of the images of the driving vehicles includes: The images of each of the vehicles are compared to identify the target vehicle that appears most frequently in the images, and this target vehicle is identified as a noise vehicle on the target road.

5. The method according to claim 3, characterized in that, Determining noisy vehicles on the target road based on each of the images of the driving vehicles includes: Each of the images of the driving vehicles contains the vehicle model; Search the preset noisy vehicle image library to see if there is a vehicle image that matches the vehicle model, and determine the noisy vehicle confidence level of each vehicle in each driving vehicle image based on the search results; Based on the noise vehicle confidence level, the noisy vehicles on the target road are determined from the individual vehicles contained in each of the driving vehicle images.

6. The method according to claim 1, characterized in that, Analyze each of the building images to determine the number of consecutively lit stairwell motion-activated lights in the target building corresponding to the building image, including: For each building image, the window area of ​​each stairwell in the building image is traversed in order from low to high. Based on the window area of ​​the stairwell, it is determined whether the voice-activated lights in each stairwell of the target building corresponding to the building image are lit. Based on the judgment result, determine the number of consecutive floors where the voice-activated lights in the stairwells of the target building corresponding to the building image are lit.

7. A noise vehicle monitoring device, characterized in that, include: The building image acquisition module is used to acquire monitoring images taken at various points on the target road within a preset time period in response to the triggering of a noise vehicle monitoring event, wherein at least one of the monitoring images contains a building image. The module for determining the number of consecutively lit layers is used to analyze each of the building images and determine the number of consecutively lit layers of the voice-activated lights in the stairwell of the target building corresponding to the building image. The noise vehicle monitoring module is used to determine that there are noisy vehicles on the target road when the number of consecutively lit layers corresponding to at least one building image is greater than a preset layer threshold.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the noise vehicle monitoring method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the noise vehicle monitoring method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the noise vehicle monitoring method according to any one of claims 1-6.

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