CN111461024A - Zombie car identification method based on intelligent vehicle-mounted terminal - Google Patents
Zombie car identification method based on intelligent vehicle-mounted terminal Download PDFInfo
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- CN111461024A CN111461024A CN202010253041.7A CN202010253041A CN111461024A CN 111461024 A CN111461024 A CN 111461024A CN 202010253041 A CN202010253041 A CN 202010253041A CN 111461024 A CN111461024 A CN 111461024A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- G06V2201/08—Detecting or categorising vehicles
Abstract
The invention discloses a zombie car identification method based on an intelligent vehicle-mounted terminal, which is characterized in that a car is identified from a video based on an image algorithm, preliminary judgment is carried out on the zombie car by utilizing longitude and latitude, speed and a threshold value, the zombie car is identified by utilizing a deep learning technology and a locally learned car image identification algorithm, and information of the zombie car is reported to relevant departments by utilizing a block chain technology, so that the problems can be handled in time. Therefore, the number of parking spaces can be correspondingly increased, and the appearance and the environment of the urban area can be improved to a certain extent.
Description
Technical Field
The invention relates to the field of big data analysis and data mining, in particular to a zombie car identification method based on an intelligent vehicle-mounted terminal.
Background
Along with the development of society, the living standard of people is continuously improved, the quantity of motor vehicles is continuously increased, corpse vehicles in cities are parked for a long time and are not maintained by people, public resources such as roads and parking spaces are occupied for a long time, the urban image is influenced, potential safety hazards such as spontaneous combustion and spontaneous explosion exist, the normal passing of the roads is influenced, and even some vehicles are parked for a long time, so that the automobile leakage is caused, and the environment is polluted. The city scope is wide, the road is many, searches for "corpse car" like big sea fishing needle, does not have clear scope, needs to consume a large amount of manpower, material resources.
Disclosure of Invention
The invention aims to provide a zombie car identification method based on an intelligent vehicle-mounted terminal.
The technical scheme adopted by the invention is as follows:
the zombie car identification method based on the intelligent vehicle-mounted terminal comprises the following steps:
the method comprises the following steps that 1, a vehicle-mounted terminal of a vehicle is used for carrying out video acquisition on passing roadside vehicles to obtain driving data with vehicle information, and the driving data are uploaded to a data center;
step 2, the data center identifies the roadside vehicle from the driving data by using an image algorithm,
step 3, judging whether the speed of the roadside vehicle is 0 or not by combining the pictures before and after the video; if yes, executing step 4; otherwise, executing step 2;
step 4, the data center acquires the longitude and latitude of the roadside vehicle, and judges whether the stay frequency of the roadside vehicle in the same longitude and latitude is greater than a set number n (n is an integer not less than 1) and the time interval from the first discovery is greater than a set threshold t (for example, t is 10 days) based on the vehicle information query database; if yes, executing step 5; otherwise, updating the database information and executing the step 2;
step 5, the data center detects video monitoring data near the roadside vehicle and judges whether the body of the roadside vehicle contains dust or leaves; if yes, extracting the vehicle condition state data of the roadside vehicle from the driving data and executing the step 6; otherwise, executing step 2;
step 6, judging whether the vehicle condition is good or bad and judging whether the vehicle is a zombie vehicle or not based on the vehicle condition state of the roadside vehicle;
and 7, uploading the corresponding video judged as the zombie car to a video monitoring platform by the data center and reporting to relevant departments.
Further, the specific steps of step 1 are: the method comprises the steps of collecting static vehicle data on the front side at a fixed time period T to obtain a driving data sequence U in the driving process of a vehicle, and sending the collected driving data sequence to a data center through a mobile cellular communication technology.
Further, the stationary vehicle data includes the position L of the vehicle, the license plate m, the time t, and the direction information d.
Further, the data center acquires the longitude and latitude of the roadside vehicle at the corresponding moment based on the driving track data of the uploading vehicle-mounted terminal.
Further, the specific steps of updating the database in step 4 are as follows:
step 4.1, searching whether other vehicle information exists in the longitude and latitude in the database; if yes, removing the vehicle information of the longitude and latitude and executing the step 2; otherwise, executing step 4.2;
step 4.2, searching whether the license plate has different longitude and latitude information in a database; if yes, clearing the vehicle information from the database and executing the step 2; otherwise, the longitude and latitude and the frequency of the vehicle information in the database are +1, the time of the first occurrence is recorded, and the step 2 is executed.
Further, the step of judging the zombie car in the step 6 comprises the following steps:
step 6.1, judging whether the vehicle body is damaged or the tires of the vehicle are flat through a deep learning technology; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.2;
step 6.2, judging whether the vehicle body has large-area rust spots or the vehicle front glass covers dust leaves in a large area by a local learning vehicle image identification method; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.3;
step 6.3, further determining whether the vehicle body is a small-area rust spot or the front glass is covered with a small-area dust leaf; if yes, judging that the roadside vehicle is a suspected corpse vehicle and executing the step 7; otherwise, judging that the roadside vehicle is not a zombie vehicle and executing the step 2.
Further, when the rust spots exceed 10% of the surface of the vehicle body, the large-area rust spots of the vehicle body are considered; the leaves of the dust cover 10% of the surface of the front glass of the vehicle.
Further, in step 7, the data center uploads videos through a 4G or 5G network.
By adopting the technical scheme, the vehicle is identified from the video based on the image algorithm, the zombie vehicle is preliminarily judged by utilizing the longitude, the latitude, the speed and the threshold value, the zombie vehicle is identified by utilizing the deep learning technology and the locally learned vehicle image identification algorithm, and the information of the zombie vehicle is reported to relevant departments by utilizing the block chain technology, so that the problems can be timely processed. Therefore, the number of parking spaces can be correspondingly increased, and the appearance and the environment of the urban area can be improved to a certain extent.
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The invention is described in further detail below with reference to the accompanying drawings and the detailed description;
fig. 1 is a schematic flow diagram of a zombie car identification method based on an intelligent vehicle-mounted terminal.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, the invention discloses a zombie car identification method based on an intelligent vehicle-mounted terminal, which comprises the following steps:
the method comprises the following steps that 1, a vehicle-mounted terminal of a vehicle is used for carrying out video acquisition on passing roadside vehicles to obtain driving data with vehicle information, and the driving data are uploaded to a data center;
step 2, the data center identifies the roadside vehicle from the driving data by using an image algorithm,
step 3, judging whether the speed of the roadside vehicle is 0 or not by combining the pictures before and after the video; if yes, executing step 4; otherwise, executing step 2;
step 4, the data center acquires the longitude and latitude of the roadside vehicle, and judges whether the stay frequency of the roadside vehicle in the same longitude and latitude is greater than a set number n and the time interval of the roadside vehicle found for the first time is greater than a set threshold t on the basis of a vehicle information query database; if yes, executing step 5; otherwise, updating the database information and executing the step 2;
step 5, the data center detects video monitoring data near the roadside vehicle and judges whether the body of the roadside vehicle contains dust or leaves; if yes, extracting the vehicle condition state data of the roadside vehicle from the driving data and executing the step 6; otherwise, executing step 2;
step 6, judging whether the vehicle condition is good or bad and judging whether the vehicle is a zombie vehicle or not based on the vehicle condition state of the roadside vehicle;
and 7, uploading the corresponding video judged as the zombie car to a video monitoring platform by the data center and reporting to relevant departments.
Further, the specific steps of step 1 are: the method comprises the steps of collecting static vehicle data on the front side at a fixed time period T to obtain a driving data sequence U in the driving process of a vehicle, and sending the collected driving data sequence to a data center through a mobile cellular communication technology.
Further, the stationary vehicle data includes the position L of the vehicle, the license plate m, the time t, and the direction information d.
Further, the data center acquires the longitude and latitude of the roadside vehicle at the corresponding moment based on the driving track data of the uploading vehicle-mounted terminal.
Further, the specific steps of updating the database information and executing step 2 in step 4 are as follows:
step 4.1, searching whether other vehicle information exists in the longitude and latitude in the database; if yes, removing the vehicle information of the longitude and latitude and executing the step 2; otherwise, executing step 4.2;
step 4.2, searching whether the license plate has different longitude and latitude information in a database; if yes, clearing the vehicle information from the database and executing the step 2; otherwise, the longitude and latitude and the frequency of the vehicle information in the database are +1, the time of the first occurrence is recorded, and the step 2 is executed.
Further, the step of judging the zombie car in the step 6 comprises the following steps:
step 6.1, judging whether the vehicle body is damaged or the tires of the vehicle are flat through a deep learning technology; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.2;
step 6.2, judging whether the vehicle body has large-area rust spots or the vehicle front glass covers dust leaves in a large area by a local learning vehicle image identification method; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.3;
step 6.3, further determining whether the vehicle body is a small-area rust spot or the front glass is covered with a small-area dust leaf; if yes, judging that the roadside vehicle is a suspected corpse vehicle and executing the step 7; otherwise, judging that the roadside vehicle is not a zombie vehicle and executing the step 2.
Further, when the rust spots exceed 10% of the surface of the vehicle body, the large-area rust spots of the vehicle body are considered; the leaves of the dust cover 10% of the surface of the front glass of the vehicle.
Further, in step 7, the data center uploads videos through a 4G or 5G network.
By adopting the technical scheme, the vehicle is identified from the video based on the image algorithm, the zombie vehicle is preliminarily judged by utilizing the longitude, the latitude, the speed and the threshold value, the zombie vehicle is identified by utilizing the deep learning technology and the locally learned vehicle image identification algorithm, and the information of the zombie vehicle is reported to relevant departments by utilizing the block chain technology, so that the problems can be timely processed. Therefore, the number of parking spaces can be correspondingly increased, and the appearance and the environment of the urban area can be improved to a certain extent.
Claims (8)
1. Zombie car identification method based on intelligent vehicle-mounted terminal is characterized by comprising the following steps: which comprises the following steps:
the method comprises the following steps that 1, a vehicle-mounted terminal of a vehicle is used for carrying out video acquisition on passing roadside vehicles to obtain driving data with vehicle information, and the driving data are uploaded to a data center;
step 2, the data center identifies the roadside vehicle from the driving data by using an image algorithm,
step 3, judging whether the speed of the roadside vehicle is 0 or not by combining the pictures before and after the video; if yes, executing step 4; otherwise, executing step 2;
step 4, the data center acquires the longitude and latitude of the roadside vehicle, and judges whether the stay frequency of the roadside vehicle in the same longitude and latitude is greater than a set number n and the time interval of the roadside vehicle found for the first time is greater than a set threshold t on the basis of a vehicle information query database; if yes, executing step 5; otherwise, updating the database information and executing the step 2;
step 5, the data center detects video monitoring data near the roadside vehicle and judges whether the body of the roadside vehicle contains dust or leaves; if yes, extracting the vehicle condition state data of the roadside vehicle from the driving data and executing the step 6; otherwise, executing step 2;
step 6, judging whether the vehicle condition is good or bad and judging whether the vehicle is a zombie vehicle or not based on the vehicle condition state of the roadside vehicle;
and 7, uploading the corresponding video judged as the zombie car to a video monitoring platform by the data center and reporting to relevant departments.
2. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 1, is characterized in that: the specific steps of the step 1 are as follows: the method comprises the steps of collecting static vehicle data on the front side at a fixed time period T to obtain a driving data sequence U in the driving process of a vehicle, and sending the collected driving data sequence to a data center through a mobile cellular communication technology.
3. The zombie car identification method based on the intelligent vehicle-mounted terminal is characterized in that the static vehicle data comprise the position L of the vehicle, the license plate m, the time t and the direction information d.
4. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 1, is characterized in that: and the data center acquires the longitude and latitude of the roadside vehicle at the corresponding moment based on the driving track data of the uploaded vehicle-mounted terminal.
5. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 1, is characterized in that: the concrete steps of updating the database in the step 4 are as follows:
step 4.1, searching whether other vehicle information exists in the longitude and latitude in the database; if yes, removing the vehicle information of the longitude and latitude and executing the step 2; otherwise, executing step 4.2;
step 4.2, searching whether the license plate has different longitude and latitude information in a database; if yes, clearing the vehicle information from the database and executing the step 2; otherwise, the longitude and latitude and the frequency of the vehicle information in the database are +1, the time of the first occurrence is recorded, and the step 2 is executed.
6. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 1, is characterized in that: the step of judging the zombie cars in the step 6 comprises the following steps:
step 6.1, judging whether the vehicle body is damaged or the tires of the vehicle are flat through a deep learning technology; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.2;
step 6.2, judging whether the vehicle body has large-area rust spots or the vehicle front glass covers dust leaves in a large area by a local learning vehicle image identification method; if so, judging that the roadside vehicle is a zombie vehicle and executing the step 7; otherwise, executing step 6.3;
step 6.3, further determining whether the vehicle body is a small-area rust spot or the front glass is covered with a small-area dust leaf; if yes, judging that the roadside vehicle is a suspected corpse vehicle and executing the step 7; otherwise, judging that the roadside vehicle is not a zombie vehicle and executing the step 2.
7. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 6, is characterized in that: when the rust spots exceed 10% of the surface of the car body, the car body is considered to have large-area rust spots; the leaves of the dust cover 10% of the surface of the front glass of the vehicle.
8. The zombie car identification method based on the intelligent vehicle-mounted terminal, according to claim 1, is characterized in that: and 7, uploading the video through a 4G or 5G network by the data center.
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| CN116416795A (en) * | 2023-04-06 | 2023-07-11 | 广东云钜网络科技有限公司 | Zombie car monitoring method and system based on traffic big data |
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