Vehicle bayonet data transmission method and system based on video monitoring system
By collecting and processing audio and video data at the vehicle checkpoint, performing cloud storage and parallel processing, the problems of low quality and processing efficiency of vehicle checkpoint monitoring data are solved, and efficient traffic management and cost reduction are achieved.
Patent Information
- Application Number
- CN202411415731.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-11
- Publication Date
- 2024-12-31
AI Technical Summary
The existing vehicle mount monitoring data is of poor quality, low processing efficiency and high cost, and it is difficult to effectively process in large-vehicle traffic scenarios.
Using a data transmission method based on a video surveillance system, the audio and video data of the vehicle mount is collected, stored in the local library in a time series, and the vehicle reference data set is uploaded to the cloud to perform super decibels, license plates and retention time detection, and abnormal vehicles are marked. , reduce video stream storage and use parallel processing to improve efficiency.
It reduces storage demand, improves the quality and processing efficiency of monitoring data, adapts to large-scale traffic scenarios, reduces monitoring costs, and provides high-quality traffic management data.
Smart Images
Figure CN119232890A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of traffic monitoring, and in particular to a vehicle checkpoint data transmission method and system based on a video monitoring system. Background Art
[0002] Vehicle checkpoints are generally specific places on the road, such as toll booths, traffic or public security checkpoints and other checkpoints. Obtaining vehicle information at vehicle checkpoints can enable standardized and orderly management of vehicles and provide effective data support for traffic management. The conventional way to obtain vehicle information at vehicle checkpoints is often to photograph and record all motor vehicles passing through the vehicle checkpoint at the vehicle checkpoint.
[0003] The video surveillance data stream is large, especially when the traffic volume is large and dense, it is often difficult to process the vehicle data in a timely and effective manner. The latency is high and the requirements for storage hardware are high. These reasons lead to the poor quality of the monitoring data provided by the vehicle checkpoints, which often require secondary processing, low processing efficiency, and high monitoring costs. For this reason, a vehicle checkpoint data transmission method and system based on a video surveillance system are proposed. There is no need to store video streams, which reduces the storage volume while providing high-quality monitoring data for traffic management, facilitates traffic management of vehicles, and further improves data processing efficiency through data parallel processing. It can better adapt to large vehicle flow scenarios and effectively reduce monitoring costs. Summary of the invention
[0004] In view of the shortcomings of the prior art, the present invention provides a vehicle checkpoint data transmission method and system based on a video surveillance system, which solves the problems that the monitoring data provided by the existing vehicle checkpoints is of poor quality, often requires secondary processing, has low processing efficiency, and has high monitoring costs.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a vehicle checkpoint data transmission method based on a video surveillance system, specifically comprising the following steps: Step 1: Collect audio and video data of vehicle checkpoints; Step 2: Store the audio and video data in a local monitoring database in time sequence; Step 3: Compare the local monitoring database to obtain the vehicle reference data set and store it in the cloud database; Step 4: Perform over-decibel detection based on the audio and video data. If over-decibel occurs, go to step 7; Step 5: Perform license plate detection based on the vehicle reference data set. If the license plate information is inconsistent, go to step 7. Step 6: Perform detention time detection based on the vehicle reference data set. If the detention time is long, go to step 7; Step 7: If an abnormality occurs, mark the vehicle as an abnormal vehicle.
[0006] The present invention is further configured as follows: the step 1 of collecting the video and audio data of the vehicle checkpoint includes: Collect image data from the front end of the vehicle, collect image data from the rear end of the vehicle, and collect sound data when the vehicle passes through the checkpoint.
[0007] The present invention is further configured as follows: the local monitoring library in step 2 includes a front-end collection library and a tail-end collection library; The front-end acquisition library is used to store the collected vehicle front-end image data in time series; The rear-end acquisition library is used to store the collected vehicle rear-end image data in time series.
[0008] The present invention is further configured as follows: the vehicle reference data set in step 3 includes a front end image intercepted from the front end image data of the vehicle, a rear end image intercepted from the rear end image data of the vehicle, and sound data of the vehicle; The front-end image and the rear-end image both contain license plate information and a watermark with time information.
[0009] The present invention is further configured as follows: the super decibel detection method in step 4 includes: Preset standard decibel number as reference decibel; Use microphone arrays to detect the real-time decibel level of sound emitted by vehicles; When the real-time sound decibel exceeds the reference decibel, it is determined that the vehicle has exceeded the decibel level. Conversely, when the real-time sound decibel is lower than or equal to the reference decibel, it is determined that the vehicle has not exceeded the decibel level.
[0010] The present invention is further configured as follows: the license plate detection method in step 5 includes: Obtain the time when the vehicle enters the vehicle checkpoint as the starting reference time, and intercept the front-end image corresponding to the starting reference time from the front-end image data of the vehicle; The time when the vehicle leaves the vehicle checkpoint is obtained as the end reference time, and the end image corresponding to the end reference time is obtained from the end image data of the vehicle; Image recognition is used to identify the license plate information in the front-end image and the rear-end image, and information comparison is performed. When the information comparison results are consistent, the license plate is judged to be normal. Conversely, when the information comparison results are inconsistent, the license plate is judged to be abnormal.
[0011] The present invention is further configured as follows: the retention time detection method in step 6 includes: Preset the standard value of detention time as a reference time; Obtain the front end image and the rear end image of the corresponding vehicle, and use the difference in time information between them as the length of time the vehicle has been detained at the vehicle checkpoint; When the detention time exceeds the reference time, it is determined that the vehicle has been detained for a long time. Conversely, when the detention time is less than or equal to the reference time, it is determined that the vehicle has not been detained for a long time.
[0012] The present invention also discloses a vehicle checkpoint data transmission system based on a video monitoring system, including a front-end video monitoring module, a rear-end video monitoring module, a high-decibel monitoring module, a front-end storage library, a rear-end storage library, a data processing center and a cloud; The front-end video monitoring module is used to collect front-end image data when a vehicle enters a vehicle checkpoint; The rear-end video monitoring module is used to collect rear-end image data when the vehicle leaves the vehicle checkpoint; The high-decibel monitoring module is used to collect sound data of the vehicle in the vehicle checkpoint; The front-end storage library is used to store front-end image data; The tail end storage is used to store the tail end image data; The data processing center is used to extract vehicle reference data sets based on the front-end image data and the rear-end image data, and to perform over-decibel detection, license plate detection and retention time detection; The cloud is used to store vehicle reference data sets.
[0013] The present invention provides a vehicle checkpoint data transmission method and system based on a video surveillance system, which has the following beneficial effects: (1) The present invention utilizes the cloud to store vehicle reference data sets instead of video streams, thereby reducing the storage capacity while providing high-quality monitoring data for traffic management. This makes it easier to manage vehicles, and further improves data processing efficiency through data parallel processing. This can better adapt to scenarios with large vehicle traffic and effectively reduce monitoring costs.
[0014] (2) The present invention obtains the front-end image and the rear-end image with the license plate information as the vehicle reference data set, which effectively reduces the data volume. At the same time, through the parallel analysis and processing of the front-end image and the rear-end image, the data processing is more efficient and rapid, and the judgment and analysis of abnormal vehicles in the vehicle checkpoint can be realized. The judgment and analysis results are directly sent to the cloud, providing high-quality and accurate reference data for traffic management. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION
[0016] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.
[0017] See also Figure 1-2 The embodiment of the present invention provides the following technical solution: a vehicle checkpoint data transmission system based on a video surveillance system, comprising: The front-end video monitoring module is connected to the front-end storage library to collect the front-end image data when the vehicle enters the vehicle checkpoint; The front-end storage library is connected to the data processing center and is used to store the front-end image data; The tail end video monitoring module is connected to the tail end storage library to collect the tail end image data when the vehicle leaves the vehicle checkpoint; The tail-end storage library is connected to the data processing center and is used to store the tail-end image data; High-decibel monitoring module, which is connected to the data processing center and is used to collect sound data of the vehicle in the vehicle checkpoint; The data processing center is connected to the cloud and is used to extract the vehicle reference data set based on the front-end image data and the rear-end image data. The vehicle reference data set includes the front-end image, the rear-end image and the sound data, and performs over-decibel detection, license plate detection and retention time detection, and uploads the vehicle reference data set to the cloud; The cloud is used to store vehicle reference datasets.
[0018] The data processing center uses parallel processing to perform over-decibel detection, license plate detection and detention time detection.
[0019] The data transmission method of the vehicle checkpoint data transmission system based on the video surveillance system specifically includes the following steps: Step 1: Collecting audio and video data of the vehicle checkpoint, including collecting image data of the front end of the vehicle, collecting image data of the rear end of the vehicle, and collecting sound data of the vehicle passing through the checkpoint, that is, collecting front-end image data of the vehicle entering the vehicle checkpoint through the front-end video monitoring module, collecting rear-end image data of the vehicle leaving the vehicle checkpoint through the rear-end video monitoring module, and collecting sound data of the vehicle in the vehicle checkpoint through the high-decibel monitoring module; Step 2: Store the audio and video data in a local monitoring library in time series. The local monitoring library includes a front-end collection library and a tail-end collection library. The front-end storage library is used as a front-end collection library to store the collected vehicle front-end image data in time series. The tail-end storage library is used as a tail-end collection library to store the collected vehicle tail-end image data in time series. The sound data of the vehicle passing the checkpoint is stored in any one of the front-end collection library and the tail-end collection library. It should be noted that after the vehicle is subsequently detected for abnormality, the relevant video data of the vehicle in the front-end collection library and the tail-end collection library will be automatically deleted, and the automatic deletion includes but is not limited to immediate deletion and time-limited deletion; Instant deletion means automatically deleting the relevant video data of the vehicle in the front-end collection library and the tail-end collection library immediately after detecting an abnormality of the vehicle; Time-limited deletion means that after abnormal detection of the vehicle, the relevant video data of the vehicle in the front-end collection library and the tail-end collection library will be automatically deleted after the set storage time. Step 3: Compare the local monitoring database to obtain the time when the vehicle enters the vehicle checkpoint as the starting reference time, capture the front image corresponding to the starting reference time from the front image data of the vehicle, obtain the time when the vehicle leaves the vehicle checkpoint as the ending reference time, obtain the rear image corresponding to the ending reference time from the rear image data of the vehicle, obtain the sound data of the vehicle between the starting reference time and the ending reference time, and store the front image, rear image and sound data as the vehicle reference data set in the cloud database; Step 4: Perform over-decibel detection based on the audio and video data. The over-decibel detection method includes: Preset standard decibel number as reference decibel; Use microphone arrays to detect the real-time decibel level of sound emitted by vehicles; When the real-time sound decibel exceeds the reference decibel, the vehicle is judged to have an over-decibel situation, and the vehicle reference data set corresponding to the vehicle is marked as over-decibel, and the process goes to step 7. Conversely, when the real-time sound decibel is lower than or equal to the reference decibel, the vehicle is judged not to have an over-decibel situation; Step 5: Use image recognition to identify the license plate information in the front image and the rear image, and compare the information. When the information comparison results are consistent, the license plate is judged to be normal. Otherwise, when the information comparison results are inconsistent, the license plate is judged to be abnormal, and the vehicle reference data set corresponding to the vehicle is marked as abnormal, and then go to step 7; Step 6: Perform detention time detection based on the vehicle reference data set. The detention time detection method includes: Preset the standard value of detention time as a reference time; Obtain the front end image and the rear end image of the corresponding vehicle, and use the difference in time information between them as the length of time the vehicle has been detained at the vehicle checkpoint; When the detention time exceeds the reference time, the vehicle is judged to have been detained for a long time, the vehicle reference data set corresponding to the vehicle is marked with a detention time excess, and the process goes to step seven; conversely, when the detention time is less than or equal to the reference time, the vehicle is judged not to have been detained for a long time.
[0020] As a detailed description, both the front image and the rear image contain license plate information and a watermark with time information, wherein the watermark time in the front image is the starting reference time, and the watermark time in the rear image is the ending reference time.
[0021] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0022] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A vehicle checkpoint data transmission method based on a video surveillance system, characterized in that: The specific steps include: Step 1: Collect audio and video data of vehicle checkpoints; Step 2: Store the audio and video data in a local monitoring database in time sequence; Step 3: Compare the local monitoring database to obtain the vehicle reference data set and store it in the cloud database; Step 4: Perform over-decibel detection based on the audio and video data. If over-decibel occurs, go to step 7; Step 5: Perform license plate detection based on the vehicle reference data set. If the license plate information is inconsistent, go to step 7. Step 6: Perform detention time detection based on the vehicle reference data set. If the detention time is long, go to step 7; Step 7: If an abnormality occurs, mark the vehicle as an abnormal vehicle.
2. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 1, characterized in that: The step 1 of collecting the video and audio data of the vehicle checkpoint includes: Collect image data from the front end of the vehicle, collect image data from the rear end of the vehicle, and collect sound data when the vehicle passes through the checkpoint.
3. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 1, characterized in that: The local monitoring library in step 2 includes a front-end collection library and a tail-end collection library; The front-end acquisition library is used to store the collected vehicle front-end image data in time series; The rear-end acquisition library is used to store the collected vehicle rear-end image data in time series.
4. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 3 is characterized in that: The vehicle reference data set in step 3 includes a front image captured from the front image data of the vehicle, a rear image captured from the rear image data of the vehicle, and sound data of the vehicle; The front-end image and the rear-end image both contain license plate information and a watermark with time information.
5. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 1, characterized in that: The super decibel detection method in step 4 includes: Preset standard decibel number as reference decibel; Use microphone arrays to detect the real-time decibel level of sound emitted by vehicles; When the real-time sound decibel exceeds the reference decibel, it is determined that the vehicle has exceeded the decibel level. Conversely, when the real-time sound decibel is lower than or equal to the reference decibel, it is determined that the vehicle has not exceeded the decibel level.
6. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 4, characterized in that: The license plate detection method in step 5 includes: Obtain the time when the vehicle enters the vehicle checkpoint as the starting reference time, and intercept the front-end image corresponding to the starting reference time from the front-end image data of the vehicle; The time when the vehicle leaves the vehicle checkpoint is obtained as the end reference time, and the end image corresponding to the end reference time is obtained from the end image data of the vehicle; Image recognition is used to identify the license plate information in the front-end image and the rear-end image, and information comparison is performed. When the information comparison results are consistent, the license plate is judged to be normal. Conversely, when the information comparison results are inconsistent, the license plate is judged to be abnormal.
7. The vehicle checkpoint data transmission method based on a video surveillance system according to claim 6, characterized in that: The retention time detection method in step 6 includes: Preset the standard value of detention time as a reference time; Obtain the front end image and the rear end image of the corresponding vehicle, and use the difference in time information between them as the length of time the vehicle has been detained at the vehicle checkpoint; When the detention time exceeds the reference time, it is determined that the vehicle has been detained for a long time. Conversely, when the detention time is less than or equal to the reference time, it is determined that the vehicle has not been detained for a long time.
8. A vehicle checkpoint data transmission system based on a video surveillance system, characterized in that: It includes front-end video monitoring module, tail-end video monitoring module, high-decibel monitoring module, front-end storage library, tail-end storage library, data processing center and cloud; The front-end video monitoring module is used to collect front-end image data when a vehicle enters a vehicle checkpoint; The rear-end video monitoring module is used to collect rear-end image data when the vehicle leaves the vehicle checkpoint; The high-decibel monitoring module is used to collect sound data of the vehicle in the vehicle checkpoint; The front-end storage library is used to store front-end image data; The tail end storage is used to store the tail end image data; The data processing center is used to extract vehicle reference data sets based on the front-end image data and the rear-end image data, and to perform over-decibel detection, license plate detection and retention time detection; The cloud is used to store vehicle reference data sets.