CN114724370B

CN114724370B - Traffic data processing method, device, electronic equipment and medium

Info

Publication number: CN114724370B
Application number: CN202210352729.XA
Authority: CN
Inventors: 王浩; 梅雨
Original assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Current assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Priority date: 2022-03-31
Filing date: 2022-03-31
Publication date: 2023-07-07
Anticipated expiration: 2042-03-31
Also published as: CN114724370A

Abstract

The disclosure provides a traffic data processing method, a device, equipment, a medium and a product, relates to the technical field of artificial intelligence, and particularly relates to the technical fields of automatic driving, intelligent traffic, image recognition and the like. The traffic data processing method comprises the following steps: determining an abnormal subsequence from a traffic data sequence based on a sequence length threshold, wherein the traffic data sequence is derived based on the first traffic sensing data; processing the abnormal subsequence based on at least one of a preset rule and second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data; based on the processed traffic data sequence, a number of traffic objects is determined.

Description

Traffic data processing method, device, electronic equipment and medium

Technical Field

The present disclosure relates to the field of artificial intelligence, and more particularly, to the technical fields of automatic driving, intelligent traffic, image recognition, and the like, and more particularly, to a traffic data processing method, apparatus, electronic device, medium, and program product.

Background

In some scenarios, it is desirable to detect traffic flow, including, for example, the number of vehicles, in order to learn about traffic conditions. In the related art, the accuracy of detecting traffic flow is low.

Disclosure of Invention

The present disclosure provides a traffic data processing method, apparatus, electronic device, storage medium, and program product.

According to an aspect of the present disclosure, there is provided a traffic data processing method including: determining an abnormal subsequence from a sequence of traffic data based on a sequence length threshold, wherein the sequence of traffic data is derived based on first traffic sensing data; processing the abnormal subsequence based on at least one of a preset rule and second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data; and determining the number of traffic objects based on the processed traffic data sequence.

According to another aspect of the present disclosure, there is provided a traffic data processing apparatus including: the device comprises a first determining module, a processing module and a second determining module. A first determining module, configured to determine an abnormal subsequence from a traffic data sequence based on a sequence length threshold, where the traffic data sequence is derived based on first traffic sensing data; the processing module is used for processing the abnormal subsequence based on at least one of a preset rule and second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data; and the second determining module is used for determining the number of traffic objects based on the processed traffic data sequence.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the traffic data processing method described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the traffic data processing method described above.

According to another aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the traffic data processing method described above.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 schematically illustrates a system architecture for traffic data processing according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a traffic data processing method according to an embodiment of the present disclosure;

FIGS. 3-4 schematically illustrate a schematic diagram of a determined abnormal subsequence according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a process anomaly sub-sequence diagram according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates a process anomaly sub-sequence diagram according to another embodiment of the present disclosure;

FIG. 7 schematically illustrates a process anomaly sub-sequence diagram according to another embodiment of the present disclosure;

FIG. 8 schematically illustrates a schematic diagram of determining a number of traffic objects according to an embodiment of the disclosure;

FIG. 9 schematically illustrates a block diagram of a traffic data processing apparatus according to an embodiment of the present disclosure; and

fig. 10 is a block diagram of an electronic device for performing traffic data processing to implement an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

Fig. 1 schematically illustrates a system architecture for traffic data processing according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include data acquisition devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide a communication link between the data acquisition devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The data acquisition devices 101, 102, 103 may be various electronic devices having data acquisition functions, including but not limited to video acquisition devices, radar acquisition devices, geomagnetic acquisition devices, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the data acquisition devices 101, 102, 103. The background management server may analyze the received data and process the result. The server 105 may also be a cloud server, i.e. the server 105 has cloud computing functionality.

It should be noted that, the traffic data processing method provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, the traffic data processing apparatus provided by the embodiments of the present disclosure may be provided in the server 105.

In one example, the data acquisition devices 101, 102, 103 include sensors, and the data acquisition devices 101, 102, 103 may transmit the acquired traffic sensing data to the server 105 over the network 104. The server 105 may process the traffic sensing data to derive the number of traffic objects.

It should be understood that the number of data acquisition devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of data acquisition devices, networks, and servers, as desired for implementation.

A traffic data processing method according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 8 in conjunction with the system architecture of fig. 1. The traffic data processing method of the embodiment of the present disclosure may be performed by, for example, a server shown in fig. 1, the server shown in fig. 1 being the same as or similar to, for example, the following electronic device.

Fig. 2 schematically illustrates a flow chart of a traffic data processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the traffic data processing method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S230.

In operation S210, an abnormal subsequence is determined from the traffic data sequence based on the sequence length threshold.

In operation S220, the abnormal subsequence is processed based on at least one of a preset rule and the second traffic sensing data, resulting in a processed traffic data sequence.

In operation S230, the number of traffic objects is determined based on the processed traffic data sequence.

The traffic data sequence is illustratively derived based on the first traffic sensing data. The first traffic sensing data includes, but is not limited to, video data, radar data, geomagnetic data.

Taking video data as an example, when the video data is acquired through a video acquisition device, designating a virtual frame on a video page, aiming at a certain frame image in the video, if a traffic object appears in the virtual frame corresponding to the frame image, obtaining a first state value aiming at the frame image, and if no traffic object appears in the virtual frame corresponding to the frame image, obtaining a second state value aiming at the frame image. That is, the first state value characterizes that the frame image includes a traffic object and the second state value characterizes that the frame image does not include a traffic object.

For a plurality of frame images in a video, a plurality of state values corresponding to the plurality of frame images one by one are acquired, and the plurality of frame images are ordered according to time, so the plurality of state values are also ordered based on time. The ordered plurality of status values is determined as a traffic data sequence.

For another example, taking radar data as an example, the radar acquisition device acquires data at a plurality of times, for each time, if a traffic object is sensed at the time, a first state value is obtained for the data at the time, and if no traffic object is sensed at the time, a second state value is obtained for the data at the time, thereby obtaining a plurality of state values, and the plurality of state values are sorted based on the acquisition time to obtain a traffic data sequence.

For example, taking geomagnetic data as an example, the geomagnetic acquisition device acquires data of a plurality of moments, for each moment, if the data of the moment represents that a traffic object is pressed to the geomagnetic acquisition device, a first state value is obtained for the data of the moment, if the data of the moment represents that no traffic object is pressed to the geomagnetic acquisition device, a second state value is obtained for the data of the moment, so that a plurality of state values are obtained, and the plurality of state values are ordered based on the acquisition moment to obtain a traffic data sequence.

After the traffic data sequence is obtained, an abnormal subsequence is determined from the traffic data sequence based on the sequence length threshold.

The sequence length threshold is illustratively related to the size of the traffic object, the speed of the traffic object, the frequency of acquisition of the first traffic sensing data. Traffic objects include, for example, vehicles, including, for example, autonomous vehicles.

For example, the length of the traffic object is 5m, the traveling speed of the traffic object is 72km/h (20 m/s), and the acquisition frequency of the first traffic sensing data is 10 frames/second. The time required for the traffic object to travel through the data acquisition device is 5 m/(20 m/s) =0.25 s, and the time corresponding to each frame is 0.1 second(s) obtained by the acquisition frequency of 10 frames/second, so that the number of frames acquired by one traffic object to travel through the data acquisition device is 0.25s/0.1 s=2.5 frames, which means that the traffic object is continuously acquired by at least more than two frames through the data acquisition device. Since each state value in the traffic data sequence corresponds one-to-one to the acquired frame data, the preset sequence length threshold may be set to 2 (frames), indicating that the traffic object is traveling past the data acquisition device when more than two consecutive frames of data are acquired for the traffic object.

Next, an abnormal subsequence is determined from the traffic data sequence based on the sequence length threshold, the abnormal subsequence including at least one status value. For example, the number of state values in the abnormal sub-sequence is equal to or less than the sequence length threshold value with respect to the number of frames to be processed, that is, the abnormal sub-sequence indicates that although the state values passed by the vehicle are collected, there is a problem of erroneous detection due to the small number of continuous state values.

After obtaining the abnormal subsequence, the abnormal subsequence may be processed based on at least one of a preset rule and the second traffic sensing data to obtain a processed traffic data sequence, e.g., adjusting a state value in the abnormal subsequence, to solve the problem of false detection. The preset rules indicate, for example, how to modify the state value or the number of modifications, etc.

The traffic scene indicated by the second traffic sensing data is illustratively associated with the traffic scene indicated by the first traffic sensing data, the second traffic sensing data comprising, for example, video data. For example, the first traffic sensing data and the second traffic observation data indicate a consistent traffic scene, which includes, for example, a collection time, a collection place, and the like.

After the processed traffic data sequence is obtained, the number of traffic objects traveling through the data acquisition device can be determined based on the processed traffic data, thereby realizing detection of traffic flow.

According to an embodiment of the present disclosure, after an abnormal sub-sequence is determined from a traffic data sequence based on a sequence length threshold, the abnormal sub-sequence is processed based on at least one of a preset rule and second traffic sensing data to solve the problem of false detection. Next, the number of traffic objects is determined based on the processed traffic data, improving the detection accuracy of the traffic flow.

Fig. 3-4 schematically illustrate a schematic diagram of determining abnormal subsequences according to an embodiment of the present disclosure.

As shown in fig. 3, the traffic data sequence comprises a plurality of status values, for example comprising a first status value a and a second status value B, the traffic data sequence being represented for example in a waveform diagram. In an example, the first state value a may be represented by a value of 1 and the second state value B may be represented by a value of 0. In fig. 3, for example, it is shown that the traffic data sequence comprises 24 status values.

Taking a preset sequence length threshold value as 2 as an example, determining a continuous state value from a plurality of state values, wherein the number of the continuous state values is lower than the sequence length threshold value, for example, the determined continuous state value comprises a 5 th state value, namely, the number of the continuous state values is 1, and the number 1 is smaller than the sequence length threshold value as 2.

Next, an abnormal subsequence is determined from the traffic data sequence based on the continuous state values. For example, consecutive state values are determined as an abnormal subsequence, i.e. the abnormal subsequence comprises the 5 th state value.

Another example anomaly sub-sequence is disclosed in fig. 4, for example, the determined plurality of consecutive state values includes a 5 th state value, a 6 th state value, a 7 th state value, each consecutive state value having a number of 1, the number of 1 being less than the sequence length threshold of 2. Next, an abnormal subsequence is determined from the traffic data sequence based on the continuous state values. For example, a plurality of consecutive state values is determined as an abnormal sub-sequence, i.e. the abnormal sub-sequence comprises a 5 th state value, a 6 th state value, a 7 th state value.

It will be appreciated that when the preset sequence length threshold is 2, the number of determined consecutive state values is 1 or 2, when the preset sequence length threshold is 3, the number of determined consecutive state values may be 1, 2 or 3, and when the preset sequence length threshold is 4, the number of determined consecutive state values may be 1, 2, 3 or 4.

According to the embodiment of the disclosure, the abnormal subsequence is determined based on the preset sequence length threshold, so that the accuracy of abnormality detection is improved, and the accuracy of traffic flow detection is improved.

FIG. 5 schematically illustrates a process anomaly sub-sequence diagram according to an embodiment of the present disclosure.

As shown in FIG. 5, the anomaly subsequence 510 includes, for example, a first state value, such as state value A. The preset rule is used to indicate that the modification operation is performed on the abnormal sub-sequence 510, for example, to indicate that the modification operation is performed once on the abnormal sub-sequence 510.

Illustratively, a first state value A in the abnormal subsequence 510 is modified to a second state value B, resulting in a modified subsequence 520, and a processed traffic data sequence is derived based on the modified subsequence 520.

For example, the 1 st to 24 th state values are sequentially detected, and when the 5 th state value and the 4 th state value are detected to be inconsistent, and the 5 th state value and the 6 th state value are detected to be inconsistent, the 5 th state value is modified from A to B when the 5 th state value is the first state value A because the continuous number of the first state values A is smaller than the sequence length threshold value 2.

According to the embodiment of the disclosure, the processed traffic data sequence can be obtained by performing one-time operation on the abnormal subsequence based on the preset rule, so that the accuracy of abnormal data processing is improved.

Fig. 6 schematically illustrates a process anomaly sub-sequence diagram according to another embodiment of the present disclosure.

As shown in fig. 6, the abnormal sub-sequence 610 includes at least one of the first state value a and the second state value B, and the abnormal sub-sequence 610 is described by taking the example that the abnormal sub-sequence 610 includes the first state value a and the second state value B. Illustratively, the preset rule is used to indicate that the abnormal sub-sequence 610 is subjected to a plurality of modification operations, for example, to indicate that the abnormal sub-sequence 610 is subjected to two modification operations.

Illustratively, modifying the first state value A in the anomalous subsequence 610 to the second state value B and modifying the second state value B in the anomalous subsequence 610 to the first state value A results in the processed anomalous subsequence 620.

Then, based on the sequence length threshold 2, a new added abnormal subsequence is determined from the processed abnormal subsequences 620, the new added abnormal subsequence including the first state value a, e.g., the determined new added abnormal subsequence is the 6 th state value. Next, the first state value a in the newly added abnormal subsequence is modified to a second state value B, resulting in a modified subsequence 630, and a processed traffic data sequence is derived based on the modified subsequence 630.

Illustratively, the abnormal subsequence 610 is first modified.

For example, the 1 st to 24 th state values are sequentially detected, and when the 5 th state value and the 4 th state value are detected to be inconsistent and the 5 th state value and the 6 th state value are detected to be inconsistent, the 5 th state value A is modified to the state value B because the continuous number 2 of the 5 th state value and the 6 th state value is less than or equal to the sequence length threshold value 2.

And continuously detecting that the 6 th state value and the 7 th state value are inconsistent, and modifying the 6 th state value B into the state value A because the continuous number 2 of the 6 th state value and the 7 th state value is less than or equal to the sequence length threshold value 2.

And continuously detecting that the 7 th state value and the 8 th state value are inconsistent, and modifying the 7 th state value A into the state value B because the continuous number 2 of the 7 th state value and the 8 th state value is less than or equal to the sequence length threshold value 2.

The processed anomalous subsequence 620 is obtained by a modification.

Illustratively, since the first modification causes the processed anomalous subsequence 620 to introduce a newly added anomalous subsequence, a second modification of the processed anomalous subsequence 620 is required.

For example, the 1 st to 24 th state values are sequentially detected, and when the 6 th state value and the 5 th state value are detected to be inconsistent, and the 6 th state value and the 7 th state value are detected to be inconsistent, the 6 th state value is modified from a to B due to the fact that the continuous number of the first state values a is smaller than the sequence length threshold 2, so that the modified subsequence 630 is obtained.

According to the embodiment of the disclosure, the processed traffic data sequence can be obtained by carrying out the modification operation on the abnormal subsequence twice based on the preset rule, and the introduction of a new abnormal subsequence in one-time processing is avoided, so that the flexibility and the accuracy of abnormal data processing are improved.

Fig. 7 schematically illustrates a process anomaly sub-sequence diagram according to another embodiment of the present disclosure.

As shown in fig. 7, the second traffic sensing data 700 includes, for example, video data including a plurality of images, which correspond to, for example, 24 status values one by one, that is, the plurality of images include 24 images.

When the plurality of

abnormal subsequences

711, 712 are detected, a plurality of first state values a, for example, 9 th and 11 th state values, may be determined from the abnormal subsequence 712 for the abnormal subsequence 712 including the first state values and the second state values.

A plurality of

sensing data

701, 702 corresponding to the plurality of first state values (9 th, 11 th state values) one-to-one is determined from the second traffic sensing data 700, the sensing data 701 being, for example, a frame image corresponding to the 9 th state value, and the sensing data 702 being, for example, a frame image corresponding to the 11 th state value.

Image recognition is performed on the plurality of

sensing data

701, 702, respectively, to determine whether traffic object identifications indicated by the plurality of

sensing data

701, 702 are identical, the traffic object including, for example, a vehicle, and the traffic object identifications including, for example, license plate information. If it is determined that the traffic object identifications indicated by the plurality of

sensing data

701, 702 are identical, it indicates that the 9 th, 10 th and 11 th state values correspond to the same traffic object, but the 10 th state value is inconsistent with the 9 th and 11 th state values due to inaccurate identification of the first traffic sensing data corresponding to the 10 th state value, at this time, the second state value B in the abnormal subsequence 712 may be modified to the first state value a, thereby obtaining the adjusted subsequence 722.

In one embodiment, if only the anomalous sub-sequence 712 is included in the traffic data sequence, processing the anomalous sub-sequence 712 based on the second traffic sense data results in a processed traffic data sequence.

According to the embodiment of the disclosure, the missed detection data (10 th state value) is corrected based on the abnormal subsequence processed by the second traffic sensing data, so that the accuracy of correcting the missed detection data is realized.

In another example, if different types of abnormal subsequences are included in the traffic data sequence, including for example, abnormal subsequence 711, abnormal subsequence 712, the abnormal subsequence 711, abnormal subsequence 712 needs to be processed based on preset rules and the second traffic sensing data.

For example, as shown in fig. 7, first, the

abnormal sub-sequences

711, 712 are processed based on the second traffic sensing data, resulting in the adjusted

abnormal sub-sequences

711, 722, in which the abnormal sub-sequence 712 satisfies the criterion of processing based on the second traffic sensing data because the abnormal sub-sequence 711 does not satisfy the criterion of processing based on the second traffic sensing data, and thus the abnormal sub-sequence 712 is corrected based on only the second traffic sensing data. Then, the adjusted

abnormal subsequences

711, 722 are processed one or more times based on the preset rules (see above), resulting in modified

subsequences

731, 722, wherein the abnormal subsequence 722 does not meet the criteria for processing based on the preset rules, and thus the abnormal subsequence 711 is modified based only on the preset rules, since the abnormal subsequence 711 meets the criteria for processing based on the preset rules. The processed traffic data sequence is then derived based on the modified

subsequences

731, 722.

According to the embodiment of the disclosure, the abnormal subsequence is processed based on the preset rule and the second traffic sensing data, and after the missed detection data (10 th state value) is corrected, the false detection data (4 th state value) is continuously corrected based on the preset rule, so that the correction accuracy of the missed detection data and the false detection data is realized.

Fig. 8 schematically illustrates a schematic diagram of determining a number of traffic objects according to an embodiment of the disclosure.

As shown in fig. 8, the processed traffic data sequence includes a first state value and a second state value, and when the number of consecutive first state values a included in the processed traffic data sequence exceeds a sequence length threshold, it is determined that a traffic object is detected.

Taking the sequence length threshold as 2 as an example, determining the target subsequences 810 and 820 from the processed traffic data sequence, wherein the target subsequences 810 and 820 comprise continuous first state values A, and the number of the continuous first state values A in the target subsequences 810 and 820 is greater than the sequence length threshold 2, namely, the number of the continuous first state values A in the target subsequence 810 is 3, and the number of the continuous first state values A in the target subsequence 820 is 4. Next, based on the number of sequences of the

target subsequences

810, 820, the number of traffic objects is determined, e.g. the number of sequences 2 of the

target subsequences

810, 820 is determined as the number of traffic objects, i.e. the target subsequence 810 corresponds to a first traffic object, i.e. the target subsequence 820 corresponds to a second traffic object.

According to the embodiment of the disclosure, after the traffic data sequence is processed, the number of the traffic objects is determined based on the processed traffic data sequence, so that the detection accuracy of the number of the traffic objects is improved.

Fig. 9 schematically illustrates a block diagram of a traffic data processing apparatus according to an embodiment of the present disclosure.

As shown in fig. 9, the traffic data processing apparatus 900 of the embodiment of the present disclosure includes, for example, a first determination module 910, a processing module 920, and a second determination module 930.

The first determination module 910 may be configured to determine an abnormal subsequence from a sequence of traffic data based on a sequence length threshold, wherein the sequence of traffic data is derived based on the first traffic sensing data. According to an embodiment of the present disclosure, the first determining module 910 may perform, for example, operation S210 described above with reference to fig. 2, which is not described herein.

The processing module 920 may be configured to process the abnormal subsequence based on at least one of a preset rule and second traffic sensing data, to obtain a processed traffic data sequence, where a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data. According to an embodiment of the present disclosure, the processing module 920 may perform, for example, operation S220 described above with reference to fig. 2, which is not described herein.

The second determination module 930 may be configured to determine a number of traffic objects based on the processed traffic data sequence. The second determining module 930 may, for example, perform operation S230 described above with reference to fig. 2 according to an embodiment of the present disclosure, which is not described herein.

According to an embodiment of the present disclosure, the abnormal subsequence includes a first state value, and a preset rule indicates to modify the abnormal subsequence; wherein, the processing module 920 includes: and the first modification sub-module is used for modifying the first state value in the abnormal sub-sequence into the second state value to obtain the processed traffic data sequence.

According to an embodiment of the present disclosure, the abnormal subsequence includes at least one of a first state value and a second state value, and the preset rule indicates to perform a plurality of modification operations on the abnormal subsequence; wherein, the processing module 920 includes: the system comprises a second modification sub-module, a first determination sub-module and a third modification sub-module. The second modification sub-module is used for modifying the first state value in the abnormal subsequence into a second state value and modifying the second state value in the abnormal subsequence into the first state value to obtain a processed abnormal subsequence; a first determining sub-module, configured to determine a new added abnormal sub-sequence from the processed abnormal sub-sequences based on a sequence length threshold, where the new added abnormal sub-sequence includes a first state value; and the third modification sub-module is used for modifying the first state value in the newly added abnormal sub-sequence into the second state value to obtain the processed traffic data sequence.

According to an embodiment of the present disclosure, the abnormal subsequence includes a first state value and a second state value; the processing module 920 includes: the second determination sub-module, the third determination sub-module, and the fourth modification sub-module. A second determining sub-module for determining a plurality of first state values from the abnormal sub-sequence; a third determining sub-module for determining a plurality of sensing data corresponding to the plurality of first state values one by one from the second traffic sensing data; and the fourth modification sub-module is used for modifying the second state value in the abnormal sub-sequence into the first state value in response to the fact that the traffic object identifiers indicated by the plurality of sensing data are consistent, and obtaining the processed traffic data sequence.

According to an embodiment of the present disclosure, the processing module 920 includes: a first processing sub-module and a second processing sub-module. The first processing sub-module is used for processing the abnormal subsequence based on the second traffic sensing data to obtain an adjusted abnormal subsequence; and the second processing sub-module is used for processing the adjusted abnormal sub-sequence based on a preset rule to obtain a processed traffic data sequence.

According to an embodiment of the present disclosure, a traffic data sequence includes a plurality of status values; the first determination module 910 includes: a fourth determination sub-module and a fifth determination sub-module. A fourth determination submodule for determining a continuous state value from a plurality of state values, wherein the number of continuous state values is lower than the sequence length threshold; and a fifth determining sub-module for determining an abnormal sub-sequence from the traffic data sequence based on the continuous state value.

According to an embodiment of the present disclosure, a processed traffic data sequence includes a first status value and a second status value; the second determining module 930 includes: a sixth determination submodule and a seventh determination submodule. A sixth determining sub-module, configured to determine a target sub-sequence from the processed traffic data sequence, where the target sub-sequence includes consecutive first state values, and a number of the consecutive first state values is greater than a sequence length threshold; and a seventh determining sub-module for determining the number of traffic objects based on the number of sequences of the target sub-sequence.

According to an embodiment of the present disclosure, the first traffic sensing data includes at least one of video data, radar data, geomagnetic data; the second traffic sensing data includes video data.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.

In the technical scheme of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

According to an embodiment of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the traffic data processing method described above.

According to an embodiment of the present disclosure, there is provided a computer program product comprising a computer program/instruction which, when executed by a processor, implements the traffic data processing method described above.

Fig. 10 shows a schematic block diagram of an example electronic device 1000 that may be used to implement embodiments of the present disclosure. Electronic device 1000 is intended to represent various forms of digital computers, such as laptops, desktops, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 10, the apparatus 1000 includes a computing unit 1001 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.

Various components in device 1000 are connected to I/O interface 1005, including: an input unit 1006 such as a keyboard, a mouse, and the like; an output unit 1007 such as various types of displays, speakers, and the like; a storage unit 1008 such as a magnetic disk, an optical disk, or the like; and communication unit 1009 such as a network card, modem, wireless communication transceiver, etc. Communication unit 1009 allows device 1000 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.

The computing unit 1001 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1001 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the respective methods and processes described above, such as a traffic data processing method. For example, in some embodiments, the traffic data processing method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1000 via ROM 1002 and/or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by computing unit 1001, one or more steps of the traffic data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform the traffic data processing method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable traffic data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A traffic data processing method, comprising:

determining an abnormal subsequence from a sequence of traffic data based on a sequence length threshold, wherein the sequence of traffic data is derived based on first traffic sensing data, the abnormal subsequence comprising a first state value and a second state value;

processing the abnormal subsequence based on a preset rule or second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data;

Determining a target subsequence from the processed traffic data sequence, wherein the target subsequence comprises consecutive first state values, the number of consecutive first state values being greater than the sequence length threshold; and

determining the number of traffic objects based on the number of sequences of the target subsequence,

the modifying operation is performed on the abnormal subsequence according to the preset rule, and when the abnormal subsequence is processed based on the preset rule, the abnormal subsequence is processed based on the preset rule or the second traffic sensing data, and the processed traffic data sequence includes: modifying the first state value in the abnormal subsequence to the second state value to obtain a processed traffic data sequence;

wherein, in the case of processing the abnormal subsequence based on the second traffic sensing data, the processing the abnormal subsequence based on a preset rule or the second traffic sensing data, to obtain a processed traffic data sequence includes: determining a plurality of first state values from the abnormal subsequence; determining a plurality of sensing data corresponding to the plurality of first state values one by one from the second traffic sensing data; and in response to determining that the traffic object identifications indicated by the plurality of sensing data are consistent, modifying the second state value in the abnormal subsequence to a first state value, and obtaining the processed traffic data sequence.

2. A traffic data processing method, comprising:

processing the abnormal subsequence based on a preset rule and second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data;

the processing the abnormal subsequence based on the preset rule and the second traffic sensing data to obtain a processed traffic data sequence includes:

processing the abnormal subsequence based on the second traffic sensing data to obtain an adjusted abnormal subsequence; and

And processing the adjusted abnormal subsequence based on the preset rule to obtain the processed traffic data sequence.

3. The method of claim 1, wherein the preset rule indicates performing a plurality of modification operations on the abnormal subsequence;

the processing the abnormal subsequence based on the preset rule or the second traffic sensing data to obtain the processed traffic data sequence further includes:

modifying the first state value in the abnormal subsequence to a second state value and modifying the second state value in the abnormal subsequence to the first state value to obtain a processed abnormal subsequence;

determining a new added abnormal subsequence from the processed abnormal subsequences based on the sequence length threshold, wherein the new added abnormal subsequence comprises a first state value; and

and modifying the first state value in the newly added abnormal subsequence to a second state value to obtain the processed traffic data sequence.

4. The method of claim 1 or 2, wherein the traffic data sequence comprises a plurality of status values; the determining an abnormal subsequence from the traffic data sequence based on the sequence length threshold comprises:

Determining a continuous state value from the plurality of state values, wherein the number of continuous state values is below the sequence length threshold; and

an abnormal subsequence is determined from the sequence of traffic data based on the continuous state values.

5. The method of claim 1 or 2, wherein the first traffic sensing data comprises at least one of video data, radar data, geomagnetic data; the second traffic sensing data includes video data.

6. A traffic data processing apparatus comprising:

a first determining module, configured to determine an abnormal subsequence from a traffic data sequence based on a sequence length threshold, where the traffic data sequence is derived based on first traffic sensing data, the abnormal subsequence including a first state value and a second state value;

the processing module is used for processing the abnormal subsequence based on a preset rule or second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data;

a sixth determining sub-module, configured to determine a target sub-sequence from the processed traffic data sequence, where the target sub-sequence includes consecutive first status values, and a number of the consecutive first status values is greater than the sequence length threshold; and

A seventh determining sub-module, configured to determine the number of traffic objects based on the number of sequences of the target sub-sequences;

wherein the preset rule indicates to modify the abnormal subsequence, and the abnormal subsequence includes a first state value when the abnormal subsequence is processed based on the preset rule, and the processing module includes: the first modification sub-module is used for modifying the first state value in the abnormal sub-sequence into a second state value to obtain a processed traffic data sequence;

wherein, in the case of processing the abnormal subsequence based on the second traffic sensing data, the abnormal subsequence includes a first state value and a second state value; the processing module comprises: a second determining sub-module for determining a plurality of first state values from the abnormal sub-sequence; a third determining sub-module for determining a plurality of sensing data corresponding to the plurality of first state values one by one from the second traffic sensing data; and a fourth modification sub-module, configured to modify the second state value in the abnormal subsequence to the first state value in response to determining that the traffic object identifiers indicated by the plurality of sensing data are consistent, and obtain the processed traffic data sequence.

7. A traffic data processing apparatus comprising:

the processing module is used for processing the abnormal subsequence based on a preset rule and second traffic sensing data to obtain a processed traffic data sequence, wherein a traffic scene indicated by the second traffic sensing data is associated with a traffic scene indicated by the first traffic sensing data;

a seventh determining sub-module for determining the number of traffic objects based on the number of sequences of the target sub-sequence,

wherein the processing module comprises:

the first processing sub-module is used for processing the abnormal subsequence based on the second traffic sensing data to obtain an adjusted abnormal subsequence; and

And the second processing sub-module is used for processing the adjusted abnormal sub-sequence based on the preset rule to obtain the processed traffic data sequence.

8. The apparatus of claim 6, wherein the preset rule indicates performing a plurality of modification operations on the abnormal subsequence;

wherein the processing module further comprises:

the second modification sub-module is used for modifying the first state value in the abnormal subsequence into a second state value and modifying the second state value in the abnormal subsequence into the first state value to obtain a processed abnormal subsequence;

a first determining sub-module, configured to determine a new added abnormal sub-sequence from the processed abnormal sub-sequences based on the sequence length threshold, where the new added abnormal sub-sequence includes a first state value; and

and the third modification sub-module is used for modifying the first state value in the newly added abnormal sub-sequence into a second state value to obtain the processed traffic data sequence.

9. The apparatus of claim 6 or 7, wherein the traffic data sequence comprises a plurality of status values; the first determining module includes:

a fourth determination submodule configured to determine a continuous state value from the plurality of state values, wherein a number of the continuous state values is below the sequence length threshold; and

And a fifth determining sub-module for determining an abnormal sub-sequence from the traffic data sequence based on the continuous state value.

10. The apparatus of claim 6 or 7, wherein the first traffic sensing data comprises at least one of video data, radar data, geomagnetic data; the second traffic sensing data includes video data.

11. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.

13. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method according to any of claims 1-5.